Predict Bike Sharing Demand with AutoGluon Template¶

This notebook is a template with each step that you need to complete for the project.

Please fill in your code where there are explicit ? markers in the notebook. You are welcome to add more cells and code as you see fit.

Once you have completed all the code implementations, please export your notebook as a HTML file so the reviews can view your code. Make sure you have all outputs correctly outputted.

File-> Export Notebook As... -> Export Notebook as HTML

There is a writeup to complete as well after all code implememtation is done. Please answer all questions and attach the necessary tables and charts. You can complete the writeup in either markdown or PDF.

Completing the code template and writeup template will cover all of the rubric points for this project.

The rubric contains "Stand Out Suggestions" for enhancing the project beyond the minimum requirements. The stand out suggestions are optional. If you decide to pursue the "stand out suggestions", you can include the code in this notebook and also discuss the results in the writeup file.

Step 1: Create an account with Kaggle¶

Create Kaggle Account and download API key¶

Below is example of steps to get the API username and key. Each student will have their own username and key.

Open account settings.
Scroll down to API and click Create New API Token.
Open up kaggle.json and use the username and key.

Step 2: Download the Kaggle dataset using the kaggle python library¶

Open up Sagemaker Studio and use starter template¶

Notebook should be using a ml.t3.medium instance (2 vCPU + 4 GiB)
Notebook should be using kernal: Python 3 (MXNet 1.8 Python 3.7 CPU Optimized)

Install packages¶

!pip install -U pip
!pip install -U setuptools wheel
!pip install -U "mxnet<2.0.0" bokeh==2.0.1
!pip install autogluon --no-cache-dir
# Without --no-cache-dir, smaller aws instances may have trouble installing

Requirement already satisfied: pip in c:\users\hp\anaconda3\envs\aws  deep learning\lib\site-packages (21.1.3)
Collecting pip
  Downloading pip-21.3.1-py3-none-any.whl (1.7 MB)
Installing collected packages: pip
  Attempting uninstall: pip
    Found existing installation: pip 21.1.3
    Uninstalling pip-21.1.3:
      Successfully uninstalled pip-21.1.3

ERROR: Could not install packages due to an OSError: [WinError 5] Access is denied: 'C:\\Users\\Public\\Documents\\Wondershare\\CreatorTemp\\pip-uninstall-3dcgrmz4\\pip.exe'
Consider using the `--user` option or check the permissions.

Requirement already satisfied: setuptools in c:\users\hp\anaconda3\envs\aws  deep learning\lib\site-packages (52.0.0.post20210125)
Collecting setuptools
  Downloading setuptools-59.4.0-py3-none-any.whl (952 kB)
Requirement already satisfied: wheel in c:\users\hp\anaconda3\envs\aws  deep learning\lib\site-packages (0.36.2)
Collecting wheel
  Using cached wheel-0.37.0-py2.py3-none-any.whl (35 kB)
Installing collected packages: wheel, setuptools
  Attempting uninstall: wheel
    Found existing installation: wheel 0.36.2
    Uninstalling wheel-0.36.2:
      Successfully uninstalled wheel-0.36.2
  Attempting uninstall: setuptools
    Found existing installation: setuptools 52.0.0.post20210125
    Uninstalling setuptools-52.0.0.post20210125:
      Successfully uninstalled setuptools-52.0.0.post20210125
Successfully installed setuptools-59.4.0 wheel-0.37.0

Setup Kaggle API Key¶

# create the .kaggle directory and an empty kaggle.json file
!mkdir -p /root/.kaggle
!touch /root/.kaggle/kaggle.json
!chmod 600 /root/.kaggle/kaggle.json

# Fill in your user name and key from creating the kaggle account and API token file
import json
kaggle_username = "FILL_IN_USERNAME"
kaggle_key = "FILL_IN_KEY"

# Save API token the kaggle.json file
with open("/root/.kaggle/kaggle.json", "w") as f:
    f.write(json.dumps({"username": kaggle_username, "key": kaggle_key}))

Download and explore dataset¶

Go to the bike sharing demand competition and agree to the terms¶

# Download the dataset, it will be in a .zip file so you'll need to unzip it as well.
!kaggle competitions download -c bike-sharing-demand
# If you already downloaded it you can use the -o command to overwrite the file
!unzip -o bike-sharing-demand.zip

import pandas as pd
from autogluon.tabular import TabularPredictor

# Create the train dataset in pandas by reading the csv
# Set the parsing of the datetime column so you can use some of the `dt` features in pandas later
train = pd.read_csv('train.csv')
train.head()

# Simple output of the train dataset to view some of the min/max/varition of the dataset features.
train.describe()

# Create the test pandas dataframe in pandas by reading the csv, remember to parse the datetime!
test = pd.read_csv('test.csv')
test.head()

# Same thing as train and test dataset
submission = pd.read_csv('sampleSubmission.csv')
submission.head()

Step 3: Train a model using AutoGluon’s Tabular Prediction¶

Requirements:

We are prediting count, so it is the label we are setting.
Ignore casual and registered columns as they are also not present in the test dataset.
Use the root_mean_squared_error as the metric to use for evaluation.
Set a time limit of 10 minutes (600 seconds).
Use the preset best_quality to focus on creating the best model.

#Drop the non-used columns and train the first model 
df = train.drop(columns=['casual', 'registered'])
predictor = TabularPredictor(label="count",eval_metric='root_mean_squared_error').fit(
    train_data=df,
    time_limit=60,
    presets="best_quality",
     
)

No path specified. Models will be saved in: "AutogluonModels/ag-20211211_105451\"
Presets specified: ['best_quality']
Beginning AutoGluon training ... Time limit = 60s
AutoGluon will save models to "AutogluonModels/ag-20211211_105451\"
AutoGluon Version:  0.3.1
Train Data Rows:    10886
Train Data Columns: 9
Preprocessing data ...
AutoGluon infers your prediction problem is: 'regression' (because dtype of label-column == int and many unique label-values observed).
	Label info (max, min, mean, stddev): (977, 1, 191.57413, 181.14445)
	If 'regression' is not the correct problem_type, please manually specify the problem_type argument in fit() (You may specify problem_type as one of: ['binary', 'multiclass', 'regression'])
Using Feature Generators to preprocess the data ...
Fitting AutoMLPipelineFeatureGenerator...
	Available Memory:                    3251.59 MB
	Train Data (Original)  Memory Usage: 1.52 MB (0.0% of available memory)
	Inferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.
	Stage 1 Generators:
		Fitting AsTypeFeatureGenerator...
			Note: Converting 2 features to boolean dtype as they only contain 2 unique values.
	Stage 2 Generators:
		Fitting FillNaFeatureGenerator...
	Stage 3 Generators:
		Fitting IdentityFeatureGenerator...
		Fitting DatetimeFeatureGenerator...
	Stage 4 Generators:
		Fitting DropUniqueFeatureGenerator...
	Types of features in original data (raw dtype, special dtypes):
		('float', [])                      : 3 | ['temp', 'atemp', 'windspeed']
		('int', [])                        : 5 | ['season', 'holiday', 'workingday', 'weather', 'humidity']
		('object', ['datetime_as_object']) : 1 | ['datetime']
	Types of features in processed data (raw dtype, special dtypes):
		('float', [])                : 3 | ['temp', 'atemp', 'windspeed']
		('int', [])                  : 3 | ['season', 'weather', 'humidity']
		('int', ['bool'])            : 2 | ['holiday', 'workingday']
		('int', ['datetime_as_int']) : 1 | ['datetime']
	0.1s = Fit runtime
	9 features in original data used to generate 9 features in processed data.
	Train Data (Processed) Memory Usage: 0.63 MB (0.0% of available memory)
Data preprocessing and feature engineering runtime = 0.12s ...
AutoGluon will gauge predictive performance using evaluation metric: 'root_mean_squared_error'
	To change this, specify the eval_metric argument of fit()
AutoGluon will fit 2 stack levels (L1 to L2) ...
Fitting 11 L1 models ...
Fitting model: KNeighborsUnif_BAG_L1 ... Training model for up to 39.91s of the 59.87s of remaining time.
	-160.4131	 = Validation score   (root_mean_squared_error)
	0.05s	 = Training   runtime
	0.11s	 = Validation runtime
Fitting model: KNeighborsDist_BAG_L1 ... Training model for up to 39.61s of the 59.57s of remaining time.
	-169.5521	 = Validation score   (root_mean_squared_error)
	0.04s	 = Training   runtime
	0.11s	 = Validation runtime
Fitting model: LightGBMXT_BAG_L1 ... Training model for up to 39.33s of the 59.29s of remaining time.
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "

[1000]	train_set's rmse: 123.806	valid_set's rmse: 134.369
[2000]	train_set's rmse: 117.412	valid_set's rmse: 133.705

C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "

[1000]	train_set's rmse: 123.42	valid_set's rmse: 141.5

C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "

[1000]	train_set's rmse: 125.097	valid_set's rmse: 128.797
[2000]	train_set's rmse: 119.003	valid_set's rmse: 127.909
[3000]	train_set's rmse: 114.63	valid_set's rmse: 127.431
[4000]	train_set's rmse: 111.295	valid_set's rmse: 126.943
[5000]	train_set's rmse: 108.576	valid_set's rmse: 126.844

	Ran out of time, early stopping on iteration 5236. Best iteration is:
	[4548]	train_set's rmse: 109.8	valid_set's rmse: 126.745
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "

[1000]	train_set's rmse: 124.131	valid_set's rmse: 138.303
[2000]	train_set's rmse: 117.833	valid_set's rmse: 137.535

C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "

[1000]	train_set's rmse: 124.871	valid_set's rmse: 128.052
[2000]	train_set's rmse: 118.547	valid_set's rmse: 127.003
[3000]	train_set's rmse: 114.124	valid_set's rmse: 126.834
[4000]	train_set's rmse: 110.645	valid_set's rmse: 126.694

Review AutoGluon's training run with ranking of models that did the best.¶

#Show a summary of the Predictor
predictor.fit_summary()

*** Summary of fit() ***
Estimated performance of each model:
                    model   score_val  pred_time_val   fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order
0     WeightedEnsemble_L3 -116.525819       4.917321  46.685643                0.000999           0.215887            3       True         10
1       LightGBMXT_BAG_L2 -116.828497       4.429621  36.058200                0.171920           5.642719            2       True          7
2         LightGBM_BAG_L2 -117.254132       4.334635  34.243237                0.076935           3.827757            2       True          8
3  RandomForestMSE_BAG_L1 -118.456660       0.323833   2.730428                0.323833           2.730428            1       True          4
4     WeightedEnsemble_L2 -118.456660       0.324832   3.041250                0.000999           0.310822            2       True          6
5  RandomForestMSE_BAG_L2 -120.029292       4.667467  36.999279                0.409767           6.583799            2       True          9
6    ExtraTreesMSE_BAG_L1 -128.733445       0.323835   1.369210                0.323835           1.369210            1       True          5
7       LightGBMXT_BAG_L1 -134.088334       3.383063  26.210903                3.383063          26.210903            1       True          3
8   KNeighborsUnif_BAG_L1 -160.413078       0.112922   0.055968                0.112922           0.055968            1       True          1
9   KNeighborsDist_BAG_L1 -169.552096       0.114047   0.048972                0.114047           0.048972            1       True          2
Number of models trained: 10
Types of models trained:
{'StackerEnsembleModel_LGB', 'StackerEnsembleModel_XT', 'StackerEnsembleModel_RF', 'WeightedEnsembleModel', 'StackerEnsembleModel_KNN'}
Bagging used: True  (with 10 folds)
Multi-layer stack-ensembling used: True  (with 3 levels)
Feature Metadata (Processed):
(raw dtype, special dtypes):
('float', [])                : 3 | ['temp', 'atemp', 'windspeed']
('int', [])                  : 3 | ['season', 'weather', 'humidity']
('int', ['bool'])            : 2 | ['holiday', 'workingday']
('int', ['datetime_as_int']) : 1 | ['datetime']
Plot summary of models saved to file: AutogluonModels/ag-20211207_124116\SummaryOfModels.html
*** End of fit() summary ***

{'model_types': {'KNeighborsUnif_BAG_L1': 'StackerEnsembleModel_KNN',
  'KNeighborsDist_BAG_L1': 'StackerEnsembleModel_KNN',
  'LightGBMXT_BAG_L1': 'StackerEnsembleModel_LGB',
  'RandomForestMSE_BAG_L1': 'StackerEnsembleModel_RF',
  'ExtraTreesMSE_BAG_L1': 'StackerEnsembleModel_XT',
  'WeightedEnsemble_L2': 'WeightedEnsembleModel',
  'LightGBMXT_BAG_L2': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L2': 'StackerEnsembleModel_LGB',
  'RandomForestMSE_BAG_L2': 'StackerEnsembleModel_RF',
  'WeightedEnsemble_L3': 'WeightedEnsembleModel'},
 'model_performance': {'KNeighborsUnif_BAG_L1': -160.4130779463501,
  'KNeighborsDist_BAG_L1': -169.55209635208928,
  'LightGBMXT_BAG_L1': -134.08833420116997,
  'RandomForestMSE_BAG_L1': -118.45666016795705,
  'ExtraTreesMSE_BAG_L1': -128.7334450621692,
  'WeightedEnsemble_L2': -118.45666016795705,
  'LightGBMXT_BAG_L2': -116.82849711714496,
  'LightGBM_BAG_L2': -117.25413238595938,
  'RandomForestMSE_BAG_L2': -120.02929221036626,
  'WeightedEnsemble_L3': -116.52581891220792},
 'model_best': 'WeightedEnsemble_L3',
 'model_paths': {'KNeighborsUnif_BAG_L1': 'AutogluonModels/ag-20211207_124116\\models\\KNeighborsUnif_BAG_L1\\',
  'KNeighborsDist_BAG_L1': 'AutogluonModels/ag-20211207_124116\\models\\KNeighborsDist_BAG_L1\\',
  'LightGBMXT_BAG_L1': 'AutogluonModels/ag-20211207_124116\\models\\LightGBMXT_BAG_L1\\',
  'RandomForestMSE_BAG_L1': 'AutogluonModels/ag-20211207_124116\\models\\RandomForestMSE_BAG_L1\\',
  'ExtraTreesMSE_BAG_L1': 'AutogluonModels/ag-20211207_124116\\models\\ExtraTreesMSE_BAG_L1\\',
  'WeightedEnsemble_L2': 'AutogluonModels/ag-20211207_124116\\models\\WeightedEnsemble_L2\\',
  'LightGBMXT_BAG_L2': 'AutogluonModels/ag-20211207_124116\\models\\LightGBMXT_BAG_L2\\',
  'LightGBM_BAG_L2': 'AutogluonModels/ag-20211207_124116\\models\\LightGBM_BAG_L2\\',
  'RandomForestMSE_BAG_L2': 'AutogluonModels/ag-20211207_124116\\models\\RandomForestMSE_BAG_L2\\',
  'WeightedEnsemble_L3': 'AutogluonModels/ag-20211207_124116\\models\\WeightedEnsemble_L3\\'},
 'model_fit_times': {'KNeighborsUnif_BAG_L1': 0.05596804618835449,
  'KNeighborsDist_BAG_L1': 0.04897189140319824,
  'LightGBMXT_BAG_L1': 26.21090292930603,
  'RandomForestMSE_BAG_L1': 2.7304279804229736,
  'ExtraTreesMSE_BAG_L1': 1.3692100048065186,
  'WeightedEnsemble_L2': 0.310821533203125,
  'LightGBMXT_BAG_L2': 5.642719268798828,
  'LightGBM_BAG_L2': 3.827756643295288,
  'RandomForestMSE_BAG_L2': 6.58379864692688,
  'WeightedEnsemble_L3': 0.21588730812072754},
 'model_pred_times': {'KNeighborsUnif_BAG_L1': 0.11292243003845215,
  'KNeighborsDist_BAG_L1': 0.11404681205749512,
  'LightGBMXT_BAG_L1': 3.383063316345215,
  'RandomForestMSE_BAG_L1': 0.32383298873901367,
  'ExtraTreesMSE_BAG_L1': 0.3238346576690674,
  'WeightedEnsemble_L2': 0.0009989738464355469,
  'LightGBMXT_BAG_L2': 0.1719202995300293,
  'LightGBM_BAG_L2': 0.0769345760345459,
  'RandomForestMSE_BAG_L2': 0.40976691246032715,
  'WeightedEnsemble_L3': 0.0009992122650146484},
 'num_bag_folds': 10,
 'max_stack_level': 3,
 'model_hyperparams': {'KNeighborsUnif_BAG_L1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True,
   'use_child_oof': True},
  'KNeighborsDist_BAG_L1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True,
   'use_child_oof': True},
  'LightGBMXT_BAG_L1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'RandomForestMSE_BAG_L1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True,
   'use_child_oof': True},
  'ExtraTreesMSE_BAG_L1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True,
   'use_child_oof': True},
  'WeightedEnsemble_L2': {'use_orig_features': False,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBMXT_BAG_L2': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L2': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'RandomForestMSE_BAG_L2': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True,
   'use_child_oof': True},
  'WeightedEnsemble_L3': {'use_orig_features': False,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True}},
 'leaderboard':                     model   score_val  pred_time_val   fit_time  \
 0     WeightedEnsemble_L3 -116.525819       4.917321  46.685643   
 1       LightGBMXT_BAG_L2 -116.828497       4.429621  36.058200   
 2         LightGBM_BAG_L2 -117.254132       4.334635  34.243237   
 3  RandomForestMSE_BAG_L1 -118.456660       0.323833   2.730428   
 4     WeightedEnsemble_L2 -118.456660       0.324832   3.041250   
 5  RandomForestMSE_BAG_L2 -120.029292       4.667467  36.999279   
 6    ExtraTreesMSE_BAG_L1 -128.733445       0.323835   1.369210   
 7       LightGBMXT_BAG_L1 -134.088334       3.383063  26.210903   
 8   KNeighborsUnif_BAG_L1 -160.413078       0.112922   0.055968   
 9   KNeighborsDist_BAG_L1 -169.552096       0.114047   0.048972   
 
    pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  \
 0                0.000999           0.215887            3       True   
 1                0.171920           5.642719            2       True   
 2                0.076935           3.827757            2       True   
 3                0.323833           2.730428            1       True   
 4                0.000999           0.310822            2       True   
 5                0.409767           6.583799            2       True   
 6                0.323835           1.369210            1       True   
 7                3.383063          26.210903            1       True   
 8                0.112922           0.055968            1       True   
 9                0.114047           0.048972            1       True   
 
    fit_order  
 0         10  
 1          7  
 2          8  
 3          4  
 4          6  
 5          9  
 6          5  
 7          3  
 8          1  
 9          2  }

#Summary of the top predictor scores 
predictor.leaderboard(df, silent=True)

#Show the features' importance scores 
predictor.feature_importance(data=df)

Computing feature importance via permutation shuffling for 9 features using 1000 rows with 3 shuffle sets...
	173.03s	= Expected runtime (57.68s per shuffle set)
	109.93s	= Actual runtime (Completed 3 of 3 shuffle sets)

Create predictions from test dataset¶

#Create prediction 
predictions = predictor.predict(test)
predictions.head()

0    100.086792
1     88.135239
2     88.135239
3    116.114746
4    116.114746
Name: count, dtype: float32

NOTE: Kaggle will reject the submission if we don't set everything to be > 0.¶

# Describe the `predictions` series to see if there are any negative values
predictions.describe()

count    6493.000000
mean      209.554092
std       124.452072
min        15.967967
25%       109.388290
50%       195.461670
75%       288.361084
max       628.322876
Name: count, dtype: float64

# How many negative values do we have?
(predictions < 0).values.any()

False

# Set them to zero
predictions[predictions<0] = 0

Set predictions to submission dataframe, save, and submit¶

submission["count"] = predictions
submission.to_csv("submission.csv", index=False)

!kaggle competitions submit -c bike-sharing-demand -f submission.csv -m "first raw submission"

Successfully submitted to Bike Sharing Demand

c:\users\hp\anaconda3\envs\aws  deep learning\lib\site-packages\cryptography\hazmat\backends\openssl\x509.py:17: CryptographyDeprecationWarning: This version of cryptography contains a temporary pyOpenSSL fallback path. Upgrade pyOpenSSL now.
  utils.DeprecatedIn35,

  0%|          | 0.00/195k [00:00<?, ?B/s]
  4%|4         | 8.00k/195k [00:00<00:05, 38.1kB/s]
 49%|####9     | 96.0k/195k [00:00<00:00, 373kB/s] 
100%|##########| 195k/195k [00:00<00:00, 583kB/s] 
100%|##########| 195k/195k [00:02<00:00, 94.1kB/s]

View submission via the command line or in the web browser under the competition's page - `My Submissions`¶

!kaggle competitions submissions -c bike-sharing-demand

fileName                       date                 description                                   status    publicScore  privateScore  
-----------------------------  -------------------  --------------------------------------------  --------  -----------  ------------  
submission.csv                 2021-12-08 11:13:27  first raw submission                          complete  1.38952      1.38952       
submission_new_hpo.csv         2021-12-08 11:06:33  new features with hyperparameters Tune        complete  0.48748      0.48748       
submission_new_hpo.csv         2021-12-08 09:14:10  new features with hyperparameters Tune        complete  0.51858      0.51858       
submission_new_hpo_ensV4.csv   2021-12-07 15:02:09  new features with hyperparameters Vקרדןםמ 4   complete  0.48961      0.48961       
submission_new_hpo.csv         2021-12-07 14:29:38  new features with hyperparameters Tune        complete  0.48338      0.48338       
submission_new_featuresV3.csv  2021-12-07 14:00:06  new featuresV3                                complete  0.45074      0.45074       
submission_new_featuresV2.csv  2021-12-07 13:39:45  new features                                  complete  0.50306      0.50306       
submission_new_features.csv    2021-12-07 13:09:48  new features                                  complete  0.51097      0.51097       
submission.csv                 2021-12-07 13:04:23  first raw submission                          complete  1.39309      1.39309       
submission_new_hpo.csv         2021-12-07 11:12:13  new features with hyperparameters Tune        complete  0.45509      0.45509       
submission_new_featuresV3.csv  2021-12-07 10:53:43  new featuresV3                                complete  0.45057      0.45057       
submission_new_featuresV2.csv  2021-12-07 10:43:05  new features                                  complete  0.47595      0.47595       
submission_new_featuresV2.csv  2021-12-07 10:42:32  new features                                  error     None         None          
submission_new_features.csv    2021-12-07 10:32:26  new features                                  complete  0.50670      0.50670       
submission.csv                 2021-12-07 10:24:27  first raw submission                          complete  1.38989      1.38989       
submission_new_hpo.csv         2021-12-07 09:08:34  new features with hyperparameters             complete  0.46172      0.46172       
submission_new_hpo.csv         2021-12-07 08:59:07  new features with hyperparameters             complete  0.45760      0.45760       
submission_new_features.csv    2021-12-06 20:57:01  new features                                  complete  0.44755      0.44755       
submission_new_features.csv    2021-12-06 20:50:18  new features                                  complete  0.44577      0.44577       
submission_new_features.csv    2021-12-06 20:45:43  new features                                  complete  0.44592      0.44592       
submission_RMSLE.csv           2021-12-06 17:57:57  Custome Eval Metric                           complete  0.46093      0.46093       
submission_RMSLE.csv           2021-12-06 17:46:31  Custome Eval Metric                           complete  0.46093      0.46093       
submission_RMSLE.csv           2021-12-06 17:25:04  Custome Eval Metric                           complete  0.46093      0.46093       
submission_RMSLE.csv           2021-12-06 17:13:44  Custome Eval Metric                           complete  0.46093      0.46093       
submission_new_hpo_ens.csv     2021-12-06 15:59:53  new features with hyperparameters             complete  0.44685      0.44685       
submission_new_hpo_ens.csv     2021-12-06 15:55:19  new features with hyperparameters             complete  0.47243      0.47243       
submission_new_hpo_ens.csv     2021-12-06 15:14:56  new features with hyperparameters             complete  0.44685      0.44685       
submission_new_hpo_ens.csv     2021-12-06 15:11:35  new features with hyperparameters             complete  0.47219      0.47219       
submission_new_hpo_ens.csv     2021-12-06 15:03:25  new features with hyperparameters             complete  0.44685      0.44685       
submission_new_hpo.csv         2021-12-06 15:02:29  new features with hyperparameters             complete  0.44685      0.44685       
submission_new_hpo.csv         2021-12-06 13:32:40  new features with hyperparameters             complete  0.44575      0.44575       
submission_new_hpo.csv         2021-12-06 12:22:42  new features with hyperparameters             complete  0.45943      0.45943       
submission_new_hpo.csv         2021-12-06 12:19:40  new features with hyperparameters             complete  0.45999      0.45999       
submission_new_hpo.csv         2021-12-06 11:38:43  new features with hyperparameters             complete  0.49236      0.49236       
submission_new_hpo.csv         2021-12-06 11:23:09  new features with hyperparameters             complete  0.44575      0.44575       
submission_new_hpo.csv         2021-12-06 11:18:36  new features with hyperparameters             complete  0.44575      0.44575       
submission_new_hpo.csv         2021-12-06 11:13:58  new features with hyperparameters             complete  0.46954      0.46954       
submission_new_features.csv    2021-12-06 10:58:08  new features                                  complete  0.44585      0.44585       
submission_new_hpo.csv         2021-12-06 10:54:45  new features with hyperparameters             complete  0.48625      0.48625       
submission_new_hpo.csv         2021-12-06 10:39:14  new features with hyperparameters             complete  0.49986      0.49986       
submission_new_hpo.csv         2021-12-06 10:35:11  new features with hyperparameters             complete  0.45109      0.45109       
submission_new_features.csv    2021-12-06 09:55:51  new features                                  complete  0.44583      0.44583       
submission_new_features.csv    2021-12-04 11:36:34  new features                                  complete  0.47874      0.47874       
submission_new_features.csv    2021-12-04 11:32:02  new features                                  complete  0.50538      0.50538       
submission_new_features.csv    2021-12-04 11:17:25  new features                                  complete  0.48208      0.48208       
submission_new_features.csv    2021-12-04 11:10:20  new features                                  error     None         None          
submission_new_features.csv    2021-12-02 10:41:10  new features                                  complete  0.48497      0.48497       
submission_new_features.csv    2021-12-02 08:13:40  new features                                  complete  0.53024      0.53024       
submission_new_features.csv    2021-12-01 11:21:00  new features                                  complete  0.52819      0.52819       
submission.csv                 2021-11-30 11:39:40  first raw submission                          complete  1.39183      1.39183

c:\users\hp\anaconda3\envs\aws  deep learning\lib\site-packages\cryptography\hazmat\backends\openssl\x509.py:17: CryptographyDeprecationWarning: This version of cryptography contains a temporary pyOpenSSL fallback path. Upgrade pyOpenSSL now.
  utils.DeprecatedIn35,

Initial score of 1.39183¶

Step 4: Exploratory Data Analysis and Creating an additional feature¶

Any additional feature will do, but a great suggestion would be to separate out the datetime into hour, day, or month parts.

import matplotlib.pyplot as plt
# Create a histogram of all features to show the distribution of each one relative to the data. This is part of the exploritory data analysis

#Select all features (Except date)
features = train.columns
features = features.drop('datetime')

fig, axs = plt.subplots(4, 3, figsize=(25, 25))
axs = axs.flatten()
ax_idx = 0

#Iterate over the features, and plot Histogram of all features and the target
for items in features : 
    ax = train[items].plot(kind = "hist",figsize=(10, 10),color='blue',fontsize = 7,bins=25,ax=axs[ax_idx])
    ax.set_facecolor('white')
    ax.set_title(items, fontdict={'fontsize': 12, 'fontweight': 'medium'})
    ax.legend(fontsize = 12)
    ax_idx += 1
     
        
ax=axs[11]
ax.axis('off')   
plt.tight_layout()
plt.show()

#BoxPlot is another way to show distribution and also check for outliers 

import seaborn as sns
sns.set_style("whitegrid")

fig, axs = plt.subplots(4, 3, figsize=(15, 12))
axs = axs.flatten()
ax_idx = 0


for items in features : 
    boxplot = sns.boxplot(y = items ,data = train ,ax = axs[ax_idx])
    boxplot.set_ylabel(items, fontsize=14)
    ax_idx +=1 
    
ax=axs[11]
ax.axis('off')

(0.0, 1.0, 0.0, 1.0)

#display Time series data of the count (demand ) 
# Check each Month and look for patterns 
from itertools import cycle

#Create  a date column
train ['date'] = pd.to_datetime(train['datetime'], errors='coerce')
color_cycle = cycle(plt.rcParams['axes.prop_cycle'].by_key()['color'])
Months = pd.date_range(start = '2011-01', end = '2012-12').to_period('M').unique()


fig, axs = plt.subplots(12, 2, figsize=(10, 25))
axs = axs.flatten()
ax_idx = 0
for item in Months:
        
        #Slice The Data for only January 2011
        mask = (train ['date'].dt.to_period('M') == pd.to_datetime(item.strftime('%Y-%m')).to_period('M')) 
        
        
        #Visualize the demand for January 2011
        demnad_data = train.loc[mask]
        demnad_data = pd.DataFrame(demnad_data[['count','date']])
        demnad_data.index = demnad_data['date']
        
        ax = demnad_data['count'].plot(figsize=(25, 38),color=next(color_cycle),fontsize = 18,ax=axs[ax_idx])
        ax.set_facecolor('white')
        ax.set_xlabel("Date",size=25)
        ax.set_ylabel("Bike Demand",size=25)
        ax.set_title('Bike Demand '+item.strftime('%Y-%m'), fontdict={'fontsize': 30, 'fontweight': 'medium'})
        
        
        ax_idx += 1
plt.tight_layout()
plt.show()

#Visualize the Hourly distribution of the demand (Boxplot)
train['hour'] = train['date'].dt.hour
plt.figure(figsize=(15,12))
boxplot = sns.boxplot(y = 'count', x = 'hour'  ,data = train )
boxplot.set_ylabel("Bike Demand", fontsize=14)

Text(0, 0.5, 'Bike Demand')

# create a new feature - First round 
train ['date'] = pd.to_datetime(train['datetime'], errors='coerce')
test ['date'] = pd.to_datetime(test['datetime'], errors='coerce')
train['hour'] = train['date'].dt.hour
test['hour'] = test['date'].dt.hour
train['weekday'] = train['date'].dt.weekday
test['weekday'] = test['date'].dt.weekday
train['day'] = train['date'].dt.day
test['day'] = test['date'].dt.day
train['month'] = train['date'].dt.month
test['month'] = test['date'].dt.month
train.head()

Make category types for these so models know they are not just numbers¶

AutoGluon originally sees these as ints, but in reality they are int representations of a category.
Setting the dtype to category will classify these as categories in AutoGluon.

categorical_features = ["hour","day","month","season","weather","holiday","workingday","weekday"]
for items in categorical_features:
    train[items] = train[items].astype("category")
    test[items] = test[items].astype("category")

# View the  new feature
train.head()

# View histogram of all features again now with the hour feature
# Create a histogram of all features to show the distribution of each one relative to the data. This is part of the exploritory data analysis

#Select all features (Except date)
features = train.columns
features = features.drop(['datetime','date'])


fig, axs = plt.subplots(5, 3, figsize=(25, 25))
axs = axs.flatten()
ax_idx = 0

#Iterate over the features, and plot Histogram of all features and the target
for items in features : 
     
    ax = train[items].astype('int').plot(kind = "hist",figsize=(15, 10),color='blue',fontsize = 7,bins=25,ax=axs[ax_idx])
    ax.set_facecolor('white')
    ax.set_title(items, fontdict={'fontsize': 15, 'fontweight': 'medium'})
    ax.legend(fontsize = 15)
    ax_idx += 1
     
        
 
plt.tight_layout()
plt.show()

Step 5: Rerun the model with the same settings as before, just with more features¶

df = train.drop(columns=['casual', 'registered'])



predictor_new_features = TabularPredictor(label="count",eval_metric='root_mean_squared_error').fit(
    train_data=df,
    time_limit=60,
    presets="best_quality"
)

No path specified. Models will be saved in: "AutogluonModels/ag-20211210_084101\"
Presets specified: ['best_quality']
Beginning AutoGluon training ... Time limit = 60s
AutoGluon will save models to "AutogluonModels/ag-20211210_084101\"
AutoGluon Version:  0.3.1
Train Data Rows:    10886
Train Data Columns: 15
Preprocessing data ...
AutoGluon infers your prediction problem is: 'regression' (because dtype of label-column == int and many unique label-values observed).
	Label info (max, min, mean, stddev): (977, 1, 191.57413, 181.14445)
	If 'regression' is not the correct problem_type, please manually specify the problem_type argument in fit() (You may specify problem_type as one of: ['binary', 'multiclass', 'regression'])
Using Feature Generators to preprocess the data ...
Fitting AutoMLPipelineFeatureGenerator...
	Available Memory:                    1953.09 MB
	Train Data (Original)  Memory Usage: 2.03 MB (0.1% of available memory)
	Inferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.
	Stage 1 Generators:
		Fitting AsTypeFeatureGenerator...
			Note: Converting 2 features to boolean dtype as they only contain 2 unique values.
	Stage 2 Generators:
		Fitting FillNaFeatureGenerator...
	Stage 3 Generators:
		Fitting IdentityFeatureGenerator...
		Fitting CategoryFeatureGenerator...
			Fitting CategoryMemoryMinimizeFeatureGenerator...
		Fitting DatetimeFeatureGenerator...
	Stage 4 Generators:
		Fitting DropUniqueFeatureGenerator...
	Useless Original Features (Count: 1): ['data']
		These features carry no predictive signal and should be manually investigated.
		This is typically a feature which has the same value for all rows.
		These features do not need to be present at inference time.
	Types of features in original data (raw dtype, special dtypes):
		('category', [])                   : 8 | ['season', 'holiday', 'workingday', 'weather', 'hour', ...]
		('datetime', [])                   : 1 | ['date']
		('float', [])                      : 3 | ['temp', 'atemp', 'windspeed']
		('int', [])                        : 1 | ['humidity']
		('object', ['datetime_as_object']) : 1 | ['datetime']
	Types of features in processed data (raw dtype, special dtypes):
		('category', [])             : 6 | ['season', 'weather', 'hour', 'weekday', 'day', ...]
		('float', [])                : 3 | ['temp', 'atemp', 'windspeed']
		('int', [])                  : 1 | ['humidity']
		('int', ['bool'])            : 2 | ['holiday', 'workingday']
		('int', ['datetime_as_int']) : 2 | ['datetime', 'date']
	0.2s = Fit runtime
	14 features in original data used to generate 14 features in processed data.
	Train Data (Processed) Memory Usage: 0.61 MB (0.0% of available memory)
Data preprocessing and feature engineering runtime = 0.24s ...
AutoGluon will gauge predictive performance using evaluation metric: 'root_mean_squared_error'
	To change this, specify the eval_metric argument of fit()
AutoGluon will fit 2 stack levels (L1 to L2) ...
Fitting 11 L1 models ...
Fitting model: KNeighborsUnif_BAG_L1 ... Training model for up to 39.83s of the 59.75s of remaining time.
	-164.89	 = Validation score   (root_mean_squared_error)
	0.04s	 = Training   runtime
	0.11s	 = Validation runtime
Fitting model: KNeighborsDist_BAG_L1 ... Training model for up to 39.53s of the 59.46s of remaining time.
	-179.1334	 = Validation score   (root_mean_squared_error)
	0.03s	 = Training   runtime
	0.11s	 = Validation runtime
Fitting model: LightGBMXT_BAG_L1 ... Training model for up to 39.26s of the 59.19s of remaining time.
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "

[1000]	train_set's rmse: 20.0349	valid_set's rmse: 33.0906

C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "

[1000]	train_set's rmse: 20.0109	valid_set's rmse: 37.3011
[2000]	train_set's rmse: 14.346	valid_set's rmse: 36.4372

	Ran out of time, early stopping on iteration 2628. Best iteration is:
	[2622]	train_set's rmse: 12.0628	valid_set's rmse: 36.2657
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "

[1000]	train_set's rmse: 19.9945	valid_set's rmse: 30.9504

C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "

[1000]	train_set's rmse: 19.8095	valid_set's rmse: 36.08
[2000]	train_set's rmse: 14.2079	valid_set's rmse: 35.7043

C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "

[1000]	train_set's rmse: 19.9756	valid_set's rmse: 35.7323
[2000]	train_set's rmse: 14.2133	valid_set's rmse: 35.0161

predictor_new_features.leaderboard(df, silent=True)

predictor_new_features.fit_summary()

*** Summary of fit() ***
Estimated performance of each model:
                    model   score_val  pred_time_val   fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order
0     WeightedEnsemble_L2  -33.593303       2.129800  33.573897                0.000000           0.338801            2       True          6
1     WeightedEnsemble_L3  -33.749492       3.340071  54.996199                0.000999           0.243859            3       True         10
2         LightGBM_BAG_L2  -33.994852       2.489593  37.692526                0.132922           4.387472            2       True          8
3       LightGBMXT_BAG_L1  -34.475003       1.423190  26.853534                1.423190          26.853534            1       True          3
4  RandomForestMSE_BAG_L2  -34.500057       2.986277  42.472419                0.629606           9.167365            2       True          9
5       LightGBMXT_BAG_L2  -34.905302       2.576544  41.197504                0.219873           7.892449            2       True          7
6    ExtraTreesMSE_BAG_L1  -37.907656       0.336821   1.946200                0.336821           1.946200            1       True          5
7  RandomForestMSE_BAG_L1  -38.415614       0.369789   4.435361                0.369789           4.435361            1       True          4
8   KNeighborsUnif_BAG_L1 -164.889972       0.113935   0.037978                0.113935           0.037978            1       True          1
9   KNeighborsDist_BAG_L1 -179.133407       0.112936   0.031981                0.112936           0.031981            1       True          2
Number of models trained: 10
Types of models trained:
{'StackerEnsembleModel_RF', 'StackerEnsembleModel_LGB', 'WeightedEnsembleModel', 'StackerEnsembleModel_KNN', 'StackerEnsembleModel_XT'}
Bagging used: True  (with 10 folds)
Multi-layer stack-ensembling used: True  (with 3 levels)
Feature Metadata (Processed):
(raw dtype, special dtypes):
('category', [])             : 6 | ['season', 'weather', 'hour', 'weekday', 'day', ...]
('float', [])                : 3 | ['temp', 'atemp', 'windspeed']
('int', [])                  : 1 | ['humidity']
('int', ['bool'])            : 2 | ['holiday', 'workingday']
('int', ['datetime_as_int']) : 2 | ['datetime', 'date']
Plot summary of models saved to file: AutogluonModels/ag-20211207_102600\SummaryOfModels.html
*** End of fit() summary ***

{'model_types': {'KNeighborsUnif_BAG_L1': 'StackerEnsembleModel_KNN',
  'KNeighborsDist_BAG_L1': 'StackerEnsembleModel_KNN',
  'LightGBMXT_BAG_L1': 'StackerEnsembleModel_LGB',
  'RandomForestMSE_BAG_L1': 'StackerEnsembleModel_RF',
  'ExtraTreesMSE_BAG_L1': 'StackerEnsembleModel_XT',
  'WeightedEnsemble_L2': 'WeightedEnsembleModel',
  'LightGBMXT_BAG_L2': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L2': 'StackerEnsembleModel_LGB',
  'RandomForestMSE_BAG_L2': 'StackerEnsembleModel_RF',
  'WeightedEnsemble_L3': 'WeightedEnsembleModel'},
 'model_performance': {'KNeighborsUnif_BAG_L1': -164.8899720329543,
  'KNeighborsDist_BAG_L1': -179.13340722310159,
  'LightGBMXT_BAG_L1': -34.475002742310096,
  'RandomForestMSE_BAG_L1': -38.41561355866655,
  'ExtraTreesMSE_BAG_L1': -37.90765646612652,
  'WeightedEnsemble_L2': -33.59330256328244,
  'LightGBMXT_BAG_L2': -34.90530180594752,
  'LightGBM_BAG_L2': -33.99485199077562,
  'RandomForestMSE_BAG_L2': -34.50005694220576,
  'WeightedEnsemble_L3': -33.74949163388618},
 'model_best': 'WeightedEnsemble_L2',
 'model_paths': {'KNeighborsUnif_BAG_L1': 'AutogluonModels/ag-20211207_102600\\models\\KNeighborsUnif_BAG_L1\\',
  'KNeighborsDist_BAG_L1': 'AutogluonModels/ag-20211207_102600\\models\\KNeighborsDist_BAG_L1\\',
  'LightGBMXT_BAG_L1': 'AutogluonModels/ag-20211207_102600\\models\\LightGBMXT_BAG_L1\\',
  'RandomForestMSE_BAG_L1': 'AutogluonModels/ag-20211207_102600\\models\\RandomForestMSE_BAG_L1\\',
  'ExtraTreesMSE_BAG_L1': 'AutogluonModels/ag-20211207_102600\\models\\ExtraTreesMSE_BAG_L1\\',
  'WeightedEnsemble_L2': 'AutogluonModels/ag-20211207_102600\\models\\WeightedEnsemble_L2\\',
  'LightGBMXT_BAG_L2': 'AutogluonModels/ag-20211207_102600\\models\\LightGBMXT_BAG_L2\\',
  'LightGBM_BAG_L2': 'AutogluonModels/ag-20211207_102600\\models\\LightGBM_BAG_L2\\',
  'RandomForestMSE_BAG_L2': 'AutogluonModels/ag-20211207_102600\\models\\RandomForestMSE_BAG_L2\\',
  'WeightedEnsemble_L3': 'AutogluonModels/ag-20211207_102600\\models\\WeightedEnsemble_L3\\'},
 'model_fit_times': {'KNeighborsUnif_BAG_L1': 0.037978172302246094,
  'KNeighborsDist_BAG_L1': 0.03198051452636719,
  'LightGBMXT_BAG_L1': 26.85353446006775,
  'RandomForestMSE_BAG_L1': 4.435361385345459,
  'ExtraTreesMSE_BAG_L1': 1.9462001323699951,
  'WeightedEnsemble_L2': 0.33880090713500977,
  'LightGBMXT_BAG_L2': 7.892449140548706,
  'LightGBM_BAG_L2': 4.387471675872803,
  'RandomForestMSE_BAG_L2': 9.167364597320557,
  'WeightedEnsemble_L3': 0.24385905265808105},
 'model_pred_times': {'KNeighborsUnif_BAG_L1': 0.11393499374389648,
  'KNeighborsDist_BAG_L1': 0.11293578147888184,
  'LightGBMXT_BAG_L1': 1.423189640045166,
  'RandomForestMSE_BAG_L1': 0.36978888511657715,
  'ExtraTreesMSE_BAG_L1': 0.3368213176727295,
  'WeightedEnsemble_L2': 0.0,
  'LightGBMXT_BAG_L2': 0.21987295150756836,
  'LightGBM_BAG_L2': 0.13292193412780762,
  'RandomForestMSE_BAG_L2': 0.6296062469482422,
  'WeightedEnsemble_L3': 0.00099945068359375},
 'num_bag_folds': 10,
 'max_stack_level': 3,
 'model_hyperparams': {'KNeighborsUnif_BAG_L1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True,
   'use_child_oof': True},
  'KNeighborsDist_BAG_L1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True,
   'use_child_oof': True},
  'LightGBMXT_BAG_L1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'RandomForestMSE_BAG_L1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True,
   'use_child_oof': True},
  'ExtraTreesMSE_BAG_L1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True,
   'use_child_oof': True},
  'WeightedEnsemble_L2': {'use_orig_features': False,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBMXT_BAG_L2': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L2': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'RandomForestMSE_BAG_L2': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True,
   'use_child_oof': True},
  'WeightedEnsemble_L3': {'use_orig_features': False,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True}},
 'leaderboard':                     model   score_val  pred_time_val   fit_time  \
 0     WeightedEnsemble_L2  -33.593303       2.129800  33.573897   
 1     WeightedEnsemble_L3  -33.749492       3.340071  54.996199   
 2         LightGBM_BAG_L2  -33.994852       2.489593  37.692526   
 3       LightGBMXT_BAG_L1  -34.475003       1.423190  26.853534   
 4  RandomForestMSE_BAG_L2  -34.500057       2.986277  42.472419   
 5       LightGBMXT_BAG_L2  -34.905302       2.576544  41.197504   
 6    ExtraTreesMSE_BAG_L1  -37.907656       0.336821   1.946200   
 7  RandomForestMSE_BAG_L1  -38.415614       0.369789   4.435361   
 8   KNeighborsUnif_BAG_L1 -164.889972       0.113935   0.037978   
 9   KNeighborsDist_BAG_L1 -179.133407       0.112936   0.031981   
 
    pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  \
 0                0.000000           0.338801            2       True   
 1                0.000999           0.243859            3       True   
 2                0.132922           4.387472            2       True   
 3                1.423190          26.853534            1       True   
 4                0.629606           9.167365            2       True   
 5                0.219873           7.892449            2       True   
 6                0.336821           1.946200            1       True   
 7                0.369789           4.435361            1       True   
 8                0.113935           0.037978            1       True   
 9                0.112936           0.031981            1       True   
 
    fit_order  
 0          6  
 1         10  
 2          8  
 3          3  
 4          9  
 5          7  
 6          5  
 7          4  
 8          1  
 9          2  }

predictor_new_features.feature_importance(data=df)

Computing feature importance via permutation shuffling for 14 features using 1000 rows with 3 shuffle sets...
	114.66s	= Expected runtime (38.22s per shuffle set)
	59.56s	= Actual runtime (Completed 3 of 3 shuffle sets)

# Remember to set all negative values to zero
predictions_nf = predictor_new_features.predict(test)
predictions_nf[predictions_nf<0] = 0

# Same submitting predictions
submission_new_features = pd.read_csv('sampleSubmission.csv')
submission_new_features["count"] = predictions_nf
submission_new_features.to_csv("submission_new_features.csv", index=False)

!kaggle competitions submit -c bike-sharing-demand -f submission_new_features.csv -m "NF - Model 1  (Basic  Time Catogricla data )"

Successfully submitted to Bike Sharing Demand

c:\users\hp\anaconda3\envs\aws  deep learning\lib\site-packages\cryptography\hazmat\backends\openssl\x509.py:17: CryptographyDeprecationWarning: This version of cryptography contains a temporary pyOpenSSL fallback path. Upgrade pyOpenSSL now.
  utils.DeprecatedIn35,

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  4%|4         | 8.00k/194k [00:00<00:03, 58.0kB/s]
 49%|####9     | 96.0k/194k [00:00<00:00, 469kB/s] 
100%|##########| 194k/194k [00:00<00:00, 674kB/s] 
100%|##########| 194k/194k [00:02<00:00, 99.0kB/s]

!kaggle competitions submissions -c bike-sharing-demand

fileName                       date                 description                                   status    publicScore  privateScore  
-----------------------------  -------------------  --------------------------------------------  --------  -----------  ------------  
submission_new_features.csv    2021-12-08 11:24:55  NF - Model 1  (Basic  Time Catogricla data )  complete  0.50674      0.50674       
submission.csv                 2021-12-08 11:13:27  first raw submission                          complete  1.38952      1.38952       
submission_new_hpo.csv         2021-12-08 11:06:33  new features with hyperparameters Tune        complete  0.48748      0.48748       
submission_new_hpo.csv         2021-12-08 09:14:10  new features with hyperparameters Tune        complete  0.51858      0.51858       
submission_new_hpo_ensV4.csv   2021-12-07 15:02:09  new features with hyperparameters Vקרדןםמ 4   complete  0.48961      0.48961       
submission_new_hpo.csv         2021-12-07 14:29:38  new features with hyperparameters Tune        complete  0.48338      0.48338       
submission_new_featuresV3.csv  2021-12-07 14:00:06  new featuresV3                                complete  0.45074      0.45074       
submission_new_featuresV2.csv  2021-12-07 13:39:45  new features                                  complete  0.50306      0.50306       
submission_new_features.csv    2021-12-07 13:09:48  new features                                  complete  0.51097      0.51097       
submission.csv                 2021-12-07 13:04:23  first raw submission                          complete  1.39309      1.39309       
submission_new_hpo.csv         2021-12-07 11:12:13  new features with hyperparameters Tune        complete  0.45509      0.45509       
submission_new_featuresV3.csv  2021-12-07 10:53:43  new featuresV3                                complete  0.45057      0.45057       
submission_new_featuresV2.csv  2021-12-07 10:43:05  new features                                  complete  0.47595      0.47595       
submission_new_featuresV2.csv  2021-12-07 10:42:32  new features                                  error     None         None          
submission_new_features.csv    2021-12-07 10:32:26  new features                                  complete  0.50670      0.50670       
submission.csv                 2021-12-07 10:24:27  first raw submission                          complete  1.38989      1.38989       
submission_new_hpo.csv         2021-12-07 09:08:34  new features with hyperparameters             complete  0.46172      0.46172       
submission_new_hpo.csv         2021-12-07 08:59:07  new features with hyperparameters             complete  0.45760      0.45760       
submission_new_features.csv    2021-12-06 20:57:01  new features                                  complete  0.44755      0.44755       
submission_new_features.csv    2021-12-06 20:50:18  new features                                  complete  0.44577      0.44577       
submission_new_features.csv    2021-12-06 20:45:43  new features                                  complete  0.44592      0.44592       
submission_RMSLE.csv           2021-12-06 17:57:57  Custome Eval Metric                           complete  0.46093      0.46093       
submission_RMSLE.csv           2021-12-06 17:46:31  Custome Eval Metric                           complete  0.46093      0.46093       
submission_RMSLE.csv           2021-12-06 17:25:04  Custome Eval Metric                           complete  0.46093      0.46093       
submission_RMSLE.csv           2021-12-06 17:13:44  Custome Eval Metric                           complete  0.46093      0.46093       
submission_new_hpo_ens.csv     2021-12-06 15:59:53  new features with hyperparameters             complete  0.44685      0.44685       
submission_new_hpo_ens.csv     2021-12-06 15:55:19  new features with hyperparameters             complete  0.47243      0.47243       
submission_new_hpo_ens.csv     2021-12-06 15:14:56  new features with hyperparameters             complete  0.44685      0.44685       
submission_new_hpo_ens.csv     2021-12-06 15:11:35  new features with hyperparameters             complete  0.47219      0.47219       
submission_new_hpo_ens.csv     2021-12-06 15:03:25  new features with hyperparameters             complete  0.44685      0.44685       
submission_new_hpo.csv         2021-12-06 15:02:29  new features with hyperparameters             complete  0.44685      0.44685       
submission_new_hpo.csv         2021-12-06 13:32:40  new features with hyperparameters             complete  0.44575      0.44575       
submission_new_hpo.csv         2021-12-06 12:22:42  new features with hyperparameters             complete  0.45943      0.45943       
submission_new_hpo.csv         2021-12-06 12:19:40  new features with hyperparameters             complete  0.45999      0.45999       
submission_new_hpo.csv         2021-12-06 11:38:43  new features with hyperparameters             complete  0.49236      0.49236       
submission_new_hpo.csv         2021-12-06 11:23:09  new features with hyperparameters             complete  0.44575      0.44575       
submission_new_hpo.csv         2021-12-06 11:18:36  new features with hyperparameters             complete  0.44575      0.44575       
submission_new_hpo.csv         2021-12-06 11:13:58  new features with hyperparameters             complete  0.46954      0.46954       
submission_new_features.csv    2021-12-06 10:58:08  new features                                  complete  0.44585      0.44585       
submission_new_hpo.csv         2021-12-06 10:54:45  new features with hyperparameters             complete  0.48625      0.48625       
submission_new_hpo.csv         2021-12-06 10:39:14  new features with hyperparameters             complete  0.49986      0.49986       
submission_new_hpo.csv         2021-12-06 10:35:11  new features with hyperparameters             complete  0.45109      0.45109       
submission_new_features.csv    2021-12-06 09:55:51  new features                                  complete  0.44583      0.44583       
submission_new_features.csv    2021-12-04 11:36:34  new features                                  complete  0.47874      0.47874       
submission_new_features.csv    2021-12-04 11:32:02  new features                                  complete  0.50538      0.50538       
submission_new_features.csv    2021-12-04 11:17:25  new features                                  complete  0.48208      0.48208       
submission_new_features.csv    2021-12-04 11:10:20  new features                                  error     None         None          
submission_new_features.csv    2021-12-02 10:41:10  new features                                  complete  0.48497      0.48497       
submission_new_features.csv    2021-12-02 08:13:40  new features                                  complete  0.53024      0.53024       
submission_new_features.csv    2021-12-01 11:21:00  new features                                  complete  0.52819      0.52819

c:\users\hp\anaconda3\envs\aws  deep learning\lib\site-packages\cryptography\hazmat\backends\openssl\x509.py:17: CryptographyDeprecationWarning: This version of cryptography contains a temporary pyOpenSSL fallback path. Upgrade pyOpenSSL now.
  utils.DeprecatedIn35,

#WindSpeed and Humidity  do not looks like normal distrbution 
# Try Log transfomiation 
import numpy as np
 

df['humidity']  = np.where(train['humidity'] !=0,np.log(train['humidity']),0)
df['windspeed']  = np.where(train['windspeed'] !=0,np.log(train['windspeed']),0)
test_log = test.copy(deep = True)
test_log['humidity']  = np.where(test['humidity'] !=0,np.log(test['humidity']),0)
test_log['windspeed']  = np.where(test['windspeed'] !=0,np.log(test['windspeed']),0)

predictor_new_features_log = TabularPredictor(label="count",eval_metric='root_mean_squared_error').fit(
    train_data=df,
    time_limit=60,
    presets="best_quality"
)

C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\pandas\core\series.py:726: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\pandas\core\series.py:726: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\pandas\core\series.py:726: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)
No path specified. Models will be saved in: "AutogluonModels/ag-20211208_120254\"
Presets specified: ['best_quality']
Beginning AutoGluon training ... Time limit = 60s
AutoGluon will save models to "AutogluonModels/ag-20211208_120254\"
AutoGluon Version:  0.3.1
Train Data Rows:    10886
Train Data Columns: 14
Preprocessing data ...
AutoGluon infers your prediction problem is: 'regression' (because dtype of label-column == int and many unique label-values observed).
	Label info (max, min, mean, stddev): (977, 1, 191.57413, 181.14445)
	If 'regression' is not the correct problem_type, please manually specify the problem_type argument in fit() (You may specify problem_type as one of: ['binary', 'multiclass', 'regression'])
Using Feature Generators to preprocess the data ...
Fitting AutoMLPipelineFeatureGenerator...
	Available Memory:                    4142.16 MB
	Train Data (Original)  Memory Usage: 1.35 MB (0.0% of available memory)
	Inferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.
	Stage 1 Generators:
		Fitting AsTypeFeatureGenerator...
			Note: Converting 2 features to boolean dtype as they only contain 2 unique values.
	Stage 2 Generators:
		Fitting FillNaFeatureGenerator...
	Stage 3 Generators:
		Fitting IdentityFeatureGenerator...
		Fitting CategoryFeatureGenerator...
			Fitting CategoryMemoryMinimizeFeatureGenerator...
		Fitting DatetimeFeatureGenerator...
	Stage 4 Generators:
		Fitting DropUniqueFeatureGenerator...
	Types of features in original data (raw dtype, special dtypes):
		('category', [])                   : 8 | ['season', 'holiday', 'workingday', 'weather', 'hour', ...]
		('datetime', [])                   : 1 | ['date']
		('float', [])                      : 4 | ['temp', 'atemp', 'humidity', 'windspeed']
		('object', ['datetime_as_object']) : 1 | ['datetime']
	Types of features in processed data (raw dtype, special dtypes):
		('category', [])             : 6 | ['season', 'weather', 'hour', 'weekday', 'day', ...]
		('float', [])                : 4 | ['temp', 'atemp', 'humidity', 'windspeed']
		('int', ['bool'])            : 2 | ['holiday', 'workingday']
		('int', ['datetime_as_int']) : 2 | ['datetime', 'date']
	0.2s = Fit runtime
	14 features in original data used to generate 14 features in processed data.
	Train Data (Processed) Memory Usage: 0.61 MB (0.0% of available memory)
Data preprocessing and feature engineering runtime = 0.19s ...
AutoGluon will gauge predictive performance using evaluation metric: 'root_mean_squared_error'
	To change this, specify the eval_metric argument of fit()
AutoGluon will fit 2 stack levels (L1 to L2) ...
Fitting 11 L1 models ...
Fitting model: KNeighborsUnif_BAG_L1 ... Training model for up to 39.86s of the 59.8s of remaining time.
	-165.4016	 = Validation score   (root_mean_squared_error)
	0.03s	 = Training   runtime
	0.11s	 = Validation runtime
Fitting model: KNeighborsDist_BAG_L1 ... Training model for up to 39.58s of the 59.52s of remaining time.
	-179.7883	 = Validation score   (root_mean_squared_error)
	0.03s	 = Training   runtime
	0.11s	 = Validation runtime
Fitting model: LightGBMXT_BAG_L1 ... Training model for up to 39.31s of the 59.25s of remaining time.
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "

[1000]	train_set's rmse: 20.0349	valid_set's rmse: 33.0906

C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "

[1000]	train_set's rmse: 20.0109	valid_set's rmse: 37.3011
[2000]	train_set's rmse: 14.346	valid_set's rmse: 36.4372
[3000]	train_set's rmse: 10.9277	valid_set's rmse: 36.1671

	Ran out of time, early stopping on iteration 3516. Best iteration is:
	[3413]	train_set's rmse: 9.87616	valid_set's rmse: 36.1206
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "

[1000]	train_set's rmse: 19.9945	valid_set's rmse: 30.9504

C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "

[1000]	train_set's rmse: 19.8095	valid_set's rmse: 36.0766
[2000]	train_set's rmse: 14.2079	valid_set's rmse: 35.7016

C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "

[1000]	train_set's rmse: 19.9756	valid_set's rmse: 35.7323
[2000]	train_set's rmse: 14.2133	valid_set's rmse: 35.0161
[3000]	train_set's rmse: 10.8298	valid_set's rmse: 34.7197

#check the dist after the transformation   
df.hist("humidity")
df.hist("windspeed")

array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000002083ACF5518>]],
      dtype=object)

predictor_new_features_log.leaderboard(df, silent=True)

# Remember to set all negative values to zero
predictions_log = predictor_new_features_log.predict(test_log)
predictions_log[predictions_log<0] = 0

# Same submitting predictions
submission_new_features = pd.read_csv('sampleSubmission.csv')
submission_new_features["count"] = predictions_log
submission_new_features.to_csv("submission_new_features_log.csv", index=False)

!kaggle competitions submit -c bike-sharing-demand -f submission_new_features_log.csv -m "NF - Model 2  (Basic  Log Transform )"
!kaggle competitions submissions -c bike-sharing-demand

Successfully submitted to Bike Sharing Demand

c:\users\hp\anaconda3\envs\aws  deep learning\lib\site-packages\cryptography\hazmat\backends\openssl\x509.py:17: CryptographyDeprecationWarning: This version of cryptography contains a temporary pyOpenSSL fallback path. Upgrade pyOpenSSL now.
  utils.DeprecatedIn35,

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  4%|4         | 8.00k/194k [00:00<00:03, 54.1kB/s]
 49%|####9     | 96.0k/194k [00:00<00:00, 448kB/s] 
100%|##########| 194k/194k [00:00<00:00, 650kB/s] 
100%|##########| 194k/194k [00:01<00:00, 120kB/s]

fileName                         date                 description                                   status    publicScore  privateScore  
-------------------------------  -------------------  --------------------------------------------  --------  -----------  ------------  
submission_new_features_log.csv  2021-12-08 12:07:08  NF - Model 2  (Basic  Log Transform )         complete  0.50789      0.50789       
submission_new_features_log.csv  2021-12-08 12:06:06  NF - Model 2  (Basic  Log Transform )         complete  0.50674      0.50674       
submission_new_features_log.csv  2021-12-08 11:34:53  NF - Model 2  (Basic  Log Transform )         complete  0.50674      0.50674       
submission_new_features.csv      2021-12-08 11:24:55  NF - Model 1  (Basic  Time Catogricla data )  complete  0.50674      0.50674       
submission.csv                   2021-12-08 11:13:27  first raw submission                          complete  1.38952      1.38952       
submission_new_hpo.csv           2021-12-08 11:06:33  new features with hyperparameters Tune        complete  0.48748      0.48748       
submission_new_hpo.csv           2021-12-08 09:14:10  new features with hyperparameters Tune        complete  0.51858      0.51858       
submission_new_hpo_ensV4.csv     2021-12-07 15:02:09  new features with hyperparameters Vקרדןםמ 4   complete  0.48961      0.48961       
submission_new_hpo.csv           2021-12-07 14:29:38  new features with hyperparameters Tune        complete  0.48338      0.48338       
submission_new_featuresV3.csv    2021-12-07 14:00:06  new featuresV3                                complete  0.45074      0.45074       
submission_new_featuresV2.csv    2021-12-07 13:39:45  new features                                  complete  0.50306      0.50306       
submission_new_features.csv      2021-12-07 13:09:48  new features                                  complete  0.51097      0.51097       
submission.csv                   2021-12-07 13:04:23  first raw submission                          complete  1.39309      1.39309       
submission_new_hpo.csv           2021-12-07 11:12:13  new features with hyperparameters Tune        complete  0.45509      0.45509       
submission_new_featuresV3.csv    2021-12-07 10:53:43  new featuresV3                                complete  0.45057      0.45057       
submission_new_featuresV2.csv    2021-12-07 10:43:05  new features                                  complete  0.47595      0.47595       
submission_new_featuresV2.csv    2021-12-07 10:42:32  new features                                  error     None         None          
submission_new_features.csv      2021-12-07 10:32:26  new features                                  complete  0.50670      0.50670       
submission.csv                   2021-12-07 10:24:27  first raw submission                          complete  1.38989      1.38989       
submission_new_hpo.csv           2021-12-07 09:08:34  new features with hyperparameters             complete  0.46172      0.46172       
submission_new_hpo.csv           2021-12-07 08:59:07  new features with hyperparameters             complete  0.45760      0.45760       
submission_new_features.csv      2021-12-06 20:57:01  new features                                  complete  0.44755      0.44755       
submission_new_features.csv      2021-12-06 20:50:18  new features                                  complete  0.44577      0.44577       
submission_new_features.csv      2021-12-06 20:45:43  new features                                  complete  0.44592      0.44592       
submission_RMSLE.csv             2021-12-06 17:57:57  Custome Eval Metric                           complete  0.46093      0.46093       
submission_RMSLE.csv             2021-12-06 17:46:31  Custome Eval Metric                           complete  0.46093      0.46093       
submission_RMSLE.csv             2021-12-06 17:25:04  Custome Eval Metric                           complete  0.46093      0.46093       
submission_RMSLE.csv             2021-12-06 17:13:44  Custome Eval Metric                           complete  0.46093      0.46093       
submission_new_hpo_ens.csv       2021-12-06 15:59:53  new features with hyperparameters             complete  0.44685      0.44685       
submission_new_hpo_ens.csv       2021-12-06 15:55:19  new features with hyperparameters             complete  0.47243      0.47243       
submission_new_hpo_ens.csv       2021-12-06 15:14:56  new features with hyperparameters             complete  0.44685      0.44685       
submission_new_hpo_ens.csv       2021-12-06 15:11:35  new features with hyperparameters             complete  0.47219      0.47219       
submission_new_hpo_ens.csv       2021-12-06 15:03:25  new features with hyperparameters             complete  0.44685      0.44685       
submission_new_hpo.csv           2021-12-06 15:02:29  new features with hyperparameters             complete  0.44685      0.44685       
submission_new_hpo.csv           2021-12-06 13:32:40  new features with hyperparameters             complete  0.44575      0.44575       
submission_new_hpo.csv           2021-12-06 12:22:42  new features with hyperparameters             complete  0.45943      0.45943       
submission_new_hpo.csv           2021-12-06 12:19:40  new features with hyperparameters             complete  0.45999      0.45999       
submission_new_hpo.csv           2021-12-06 11:38:43  new features with hyperparameters             complete  0.49236      0.49236       
submission_new_hpo.csv           2021-12-06 11:23:09  new features with hyperparameters             complete  0.44575      0.44575       
submission_new_hpo.csv           2021-12-06 11:18:36  new features with hyperparameters             complete  0.44575      0.44575       
submission_new_hpo.csv           2021-12-06 11:13:58  new features with hyperparameters             complete  0.46954      0.46954       
submission_new_features.csv      2021-12-06 10:58:08  new features                                  complete  0.44585      0.44585       
submission_new_hpo.csv           2021-12-06 10:54:45  new features with hyperparameters             complete  0.48625      0.48625       
submission_new_hpo.csv           2021-12-06 10:39:14  new features with hyperparameters             complete  0.49986      0.49986       
submission_new_hpo.csv           2021-12-06 10:35:11  new features with hyperparameters             complete  0.45109      0.45109       
submission_new_features.csv      2021-12-06 09:55:51  new features                                  complete  0.44583      0.44583       
submission_new_features.csv      2021-12-04 11:36:34  new features                                  complete  0.47874      0.47874       
submission_new_features.csv      2021-12-04 11:32:02  new features                                  complete  0.50538      0.50538       
submission_new_features.csv      2021-12-04 11:17:25  new features                                  complete  0.48208      0.48208       
submission_new_features.csv      2021-12-04 11:10:20  new features                                  error     None         None

c:\users\hp\anaconda3\envs\aws  deep learning\lib\site-packages\cryptography\hazmat\backends\openssl\x509.py:17: CryptographyDeprecationWarning: This version of cryptography contains a temporary pyOpenSSL fallback path. Upgrade pyOpenSSL now.
  utils.DeprecatedIn35,

Step 5A: Rerun the model with some more advanced features¶

# Add the predictions to test (as count )
# Mark train and test rows to be able to split later on 
#Combine train and test for on data frame , to create the Demand Time series features 
train['data']= 'train'
test['data'] = 'test'
test['count'] = predictions_nf
raw =  pd.concat([train,test])
raw = raw.drop(['casual','registered'],axis=1)

raw.head()

#The first set of Time series features will shift features 

#raw.sort_values(by = ['hour','datetime'],inplace=True)
raw.datetime = pd.to_datetime(raw.datetime)
raw.index = raw.datetime
raw.head()

#Shift features 
raw["shift_1"] = raw['count'].shift(1, freq='D')
raw["shift_2"] = raw['count'].shift(1, freq='2D')
raw["shift_7"] = raw['count'].shift(1, freq='7D')
raw["shift_30"] = raw['count'].shift(1, freq='30D')

# Visualization - Shift Features¶

demnad_data = raw[['date','count',"shift_1","shift_2","shift_7","shift_30"]].copy(deep = True)
#demnad_data.reset_index(inplace = True)
demnad_data.sort_values(by = ['date'],inplace=True)
demnad_data.index = demnad_data.date

fig, axs = plt.subplots(3, 1, figsize=(60, 70))
axs = axs.flatten()
ax_idx = 0
fig.tight_layout(pad=10)
fig.subplots_adjust(hspace=1)

#demnad_data.index = demnad_data['date']
#Plot a shifted example to verify the shift 

ax = demnad_data[demnad_data['date']<'2011-01-2']['count'].plot(figsize=(30, 20),fontsize = 22,color = 'black',ax=axs[0])
ax = demnad_data[demnad_data['date']<'2011-01-3']['shift_1'].plot(figsize=(40, 20),fontsize = 22,color = 'red',ax=axs[0])
ax.set_title("Shift By one Day", fontdict={'fontsize': 40, 'fontweight': 'medium'})
ax.legend(fontsize = 30)

ax = demnad_data[demnad_data['date']<'2011-01-7']['count'].plot(figsize=(30, 20),fontsize = 22,color = 'black',ax=axs[1])
ax = demnad_data[demnad_data['date']<'2011-01-14']['shift_7'].plot(figsize=(40, 20),fontsize = 22,color = 'red',ax=axs[1])
ax.set_title("Shift By Week", fontdict={'fontsize': 40, 'fontweight': 'medium'})
ax.legend(fontsize = 30)

ax = demnad_data[demnad_data['date']<'2011-01-30']['count'].plot(figsize=(30, 20),fontsize = 22,color = 'black',ax=axs[2])
ax = demnad_data[demnad_data['date']<'2011-02-28']['shift_30'].plot(figsize=(40, 20),fontsize = 22,color = 'red',ax=axs[2])
ax.set_title("Shift By a Month ", fontdict={'fontsize': 40, 'fontweight': 'medium'})
ax.legend(fontsize = 30)

<matplotlib.legend.Legend at 0x29743ae1048>

raw.reset_index(inplace=True,drop=True)
train = raw[raw['data']=='train'].copy(deep=True)
test = raw[raw['data']=='test'].copy(deep=True)
test.reset_index(inplace=True,drop=True)
train.drop(['data'],axis=1,inplace =True)
test.drop(['data'],axis=1,inplace =True)

categorical_features = ["hour","day","month","season","weather","holiday","workingday","weekday"]
for items in categorical_features:
    train[items] = train[items].astype("category")
    test[items] = test[items].astype("category")

test.head()

predictor_new_features_advnced1 = TabularPredictor(label="count",eval_metric='root_mean_squared_error').fit(
    train_data=train,
    time_limit=60,
    presets="best_quality"
)

No path specified. Models will be saved in: "AutogluonModels/ag-20211208_121656\"
Presets specified: ['best_quality']
Beginning AutoGluon training ... Time limit = 60s
AutoGluon will save models to "AutogluonModels/ag-20211208_121656\"
AutoGluon Version:  0.3.1
Train Data Rows:    10886
Train Data Columns: 18
Preprocessing data ...
AutoGluon infers your prediction problem is: 'regression' (because dtype of label-column == float and many unique label-values observed).
	Label info (max, min, mean, stddev): (977.0, 1.0, 191.57413, 181.14445)
	If 'regression' is not the correct problem_type, please manually specify the problem_type argument in fit() (You may specify problem_type as one of: ['binary', 'multiclass', 'regression'])
Using Feature Generators to preprocess the data ...
Fitting AutoMLPipelineFeatureGenerator...
	Available Memory:                    4112.71 MB
	Train Data (Original)  Memory Usage: 0.96 MB (0.0% of available memory)
	Inferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.
	Stage 1 Generators:
		Fitting AsTypeFeatureGenerator...
			Note: Converting 2 features to boolean dtype as they only contain 2 unique values.
	Stage 2 Generators:
		Fitting FillNaFeatureGenerator...
	Stage 3 Generators:
		Fitting IdentityFeatureGenerator...
		Fitting CategoryFeatureGenerator...
			Fitting CategoryMemoryMinimizeFeatureGenerator...
		Fitting DatetimeFeatureGenerator...
	Stage 4 Generators:
		Fitting DropUniqueFeatureGenerator...
	Types of features in original data (raw dtype, special dtypes):
		('category', []) : 8 | ['season', 'holiday', 'workingday', 'weather', 'hour', ...]
		('datetime', []) : 2 | ['datetime', 'date']
		('float', [])    : 7 | ['temp', 'atemp', 'windspeed', 'shift_1', 'shift_2', ...]
		('int', [])      : 1 | ['humidity']
	Types of features in processed data (raw dtype, special dtypes):
		('category', [])             : 6 | ['season', 'weather', 'hour', 'weekday', 'day', ...]
		('float', [])                : 7 | ['temp', 'atemp', 'windspeed', 'shift_1', 'shift_2', ...]
		('int', [])                  : 1 | ['humidity']
		('int', ['bool'])            : 2 | ['holiday', 'workingday']
		('int', ['datetime_as_int']) : 2 | ['datetime', 'date']
	0.4s = Fit runtime
	18 features in original data used to generate 18 features in processed data.
	Train Data (Processed) Memory Usage: 0.96 MB (0.0% of available memory)
Data preprocessing and feature engineering runtime = 0.42s ...
AutoGluon will gauge predictive performance using evaluation metric: 'root_mean_squared_error'
	To change this, specify the eval_metric argument of fit()
AutoGluon will fit 2 stack levels (L1 to L2) ...
Fitting 11 L1 models ...
Fitting model: KNeighborsUnif_BAG_L1 ... Training model for up to 39.71s of the 59.57s of remaining time.
	-72.6859	 = Validation score   (root_mean_squared_error)
	0.06s	 = Training   runtime
	0.11s	 = Validation runtime
Fitting model: KNeighborsDist_BAG_L1 ... Training model for up to 39.39s of the 59.26s of remaining time.
	-72.135	 = Validation score   (root_mean_squared_error)
	0.06s	 = Training   runtime
	0.11s	 = Validation runtime
Fitting model: LightGBMXT_BAG_L1 ... Training model for up to 39.08s of the 58.95s of remaining time.
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "

[1000]	train_set's rmse: 18.4735	valid_set's rmse: 33.5858
[2000]	train_set's rmse: 12.2684	valid_set's rmse: 32.9556

	Ran out of time, early stopping on iteration 2350. Best iteration is:
	[2228]	train_set's rmse: 11.3215	valid_set's rmse: 32.865
	Time limit exceeded... Skipping LightGBMXT_BAG_L1.
Fitting model: LightGBM_BAG_L1 ... Training model for up to 34.99s of the 54.85s of remaining time.
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "

[1000]	train_set's rmse: 11.8513	valid_set's rmse: 37.8223

	Ran out of time, early stopping on iteration 1751. Best iteration is:
	[1693]	train_set's rmse: 7.08644	valid_set's rmse: 37.4643
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "

[1000]	train_set's rmse: 11.8429	valid_set's rmse: 39.953

	Ran out of time, early stopping on iteration 1848. Best iteration is:
	[1678]	train_set's rmse: 7.09089	valid_set's rmse: 39.3845
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "

[1000]	train_set's rmse: 11.9154	valid_set's rmse: 33.991

	Ran out of time, early stopping on iteration 1756. Best iteration is:
	[1754]	train_set's rmse: 6.73913	valid_set's rmse: 33.6333
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "

[1000]	train_set's rmse: 11.9578	valid_set's rmse: 38.9479

predictor_new_features_advnced1.leaderboard(train, silent=True)

predictor_new_features_advnced1.feature_importance(data=train)

Computing feature importance via permutation shuffling for 18 features using 1000 rows with 3 shuffle sets...
	96.42s	= Expected runtime (32.14s per shuffle set)
	14.41s	= Actual runtime (Completed 3 of 3 shuffle sets)

predictor_new_features_advnced1.fit_summary()

*** Summary of fit() ***
Estimated performance of each model:
                    model  score_val  pred_time_val   fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order
0     WeightedEnsemble_L3 -37.668018       2.450974  58.355911                0.000999           0.223883            3       True          8
1       LightGBMXT_BAG_L2 -38.202572       1.816336  43.267025                0.455793          11.735239            2       True          5
2     WeightedEnsemble_L2 -38.245361       1.136365  31.659730                0.000000           0.249873            2       True          4
3         LightGBM_BAG_L1 -38.245361       1.136365  31.409857                1.136365          31.409857            1       True          3
4         LightGBM_BAG_L2 -38.912343       1.497464  36.941634                0.136920           5.409848            2       True          6
5  RandomForestMSE_BAG_L2 -39.092819       1.857261  40.986941                0.496718           9.455155            2       True          7
6   KNeighborsDist_BAG_L1 -72.135046       0.112256   0.061965                0.112256           0.061965            1       True          2
7   KNeighborsUnif_BAG_L1 -72.685921       0.111923   0.059965                0.111923           0.059965            1       True          1
Number of models trained: 8
Types of models trained:
{'StackerEnsembleModel_RF', 'StackerEnsembleModel_LGB', 'StackerEnsembleModel_KNN', 'WeightedEnsembleModel'}
Bagging used: True  (with 10 folds)
Multi-layer stack-ensembling used: True  (with 3 levels)
Feature Metadata (Processed):
(raw dtype, special dtypes):
('category', [])             : 6 | ['season', 'weather', 'hour', 'weekday', 'day', ...]
('float', [])                : 7 | ['temp', 'atemp', 'windspeed', 'shift_1', 'shift_2', ...]
('int', [])                  : 1 | ['humidity']
('int', ['bool'])            : 2 | ['holiday', 'workingday']
('int', ['datetime_as_int']) : 2 | ['datetime', 'date']
Plot summary of models saved to file: AutogluonModels/ag-20211208_121656\SummaryOfModels.html
*** End of fit() summary ***

{'model_types': {'KNeighborsUnif_BAG_L1': 'StackerEnsembleModel_KNN',
  'KNeighborsDist_BAG_L1': 'StackerEnsembleModel_KNN',
  'LightGBM_BAG_L1': 'StackerEnsembleModel_LGB',
  'WeightedEnsemble_L2': 'WeightedEnsembleModel',
  'LightGBMXT_BAG_L2': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L2': 'StackerEnsembleModel_LGB',
  'RandomForestMSE_BAG_L2': 'StackerEnsembleModel_RF',
  'WeightedEnsemble_L3': 'WeightedEnsembleModel'},
 'model_performance': {'KNeighborsUnif_BAG_L1': -72.68592140879569,
  'KNeighborsDist_BAG_L1': -72.13504591728454,
  'LightGBM_BAG_L1': -38.24536147209494,
  'WeightedEnsemble_L2': -38.24536147209494,
  'LightGBMXT_BAG_L2': -38.202572338952905,
  'LightGBM_BAG_L2': -38.91234325157726,
  'RandomForestMSE_BAG_L2': -39.09281852368373,
  'WeightedEnsemble_L3': -37.66801834297829},
 'model_best': 'WeightedEnsemble_L3',
 'model_paths': {'KNeighborsUnif_BAG_L1': 'AutogluonModels/ag-20211208_121656\\models\\KNeighborsUnif_BAG_L1\\',
  'KNeighborsDist_BAG_L1': 'AutogluonModels/ag-20211208_121656\\models\\KNeighborsDist_BAG_L1\\',
  'LightGBM_BAG_L1': 'AutogluonModels/ag-20211208_121656\\models\\LightGBM_BAG_L1\\',
  'WeightedEnsemble_L2': 'AutogluonModels/ag-20211208_121656\\models\\WeightedEnsemble_L2\\',
  'LightGBMXT_BAG_L2': 'AutogluonModels/ag-20211208_121656\\models\\LightGBMXT_BAG_L2\\',
  'LightGBM_BAG_L2': 'AutogluonModels/ag-20211208_121656\\models\\LightGBM_BAG_L2\\',
  'RandomForestMSE_BAG_L2': 'AutogluonModels/ag-20211208_121656\\models\\RandomForestMSE_BAG_L2\\',
  'WeightedEnsemble_L3': 'AutogluonModels/ag-20211208_121656\\models\\WeightedEnsemble_L3\\'},
 'model_fit_times': {'KNeighborsUnif_BAG_L1': 0.059964656829833984,
  'KNeighborsDist_BAG_L1': 0.06196475028991699,
  'LightGBM_BAG_L1': 31.409857034683228,
  'WeightedEnsemble_L2': 0.24987316131591797,
  'LightGBMXT_BAG_L2': 11.735238552093506,
  'LightGBM_BAG_L2': 5.409847736358643,
  'RandomForestMSE_BAG_L2': 9.45515489578247,
  'WeightedEnsemble_L3': 0.22388315200805664},
 'model_pred_times': {'KNeighborsUnif_BAG_L1': 0.1119232177734375,
  'KNeighborsDist_BAG_L1': 0.11225557327270508,
  'LightGBM_BAG_L1': 1.1363646984100342,
  'WeightedEnsemble_L2': 0.0,
  'LightGBMXT_BAG_L2': 0.4557926654815674,
  'LightGBM_BAG_L2': 0.13692045211791992,
  'RandomForestMSE_BAG_L2': 0.4967179298400879,
  'WeightedEnsemble_L3': 0.0009992122650146484},
 'num_bag_folds': 10,
 'max_stack_level': 3,
 'model_hyperparams': {'KNeighborsUnif_BAG_L1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True,
   'use_child_oof': True},
  'KNeighborsDist_BAG_L1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True,
   'use_child_oof': True},
  'LightGBM_BAG_L1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'WeightedEnsemble_L2': {'use_orig_features': False,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBMXT_BAG_L2': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L2': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'RandomForestMSE_BAG_L2': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True,
   'use_child_oof': True},
  'WeightedEnsemble_L3': {'use_orig_features': False,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True}},
 'leaderboard':                     model  score_val  pred_time_val   fit_time  \
 0     WeightedEnsemble_L3 -37.668018       2.450974  58.355911   
 1       LightGBMXT_BAG_L2 -38.202572       1.816336  43.267025   
 2     WeightedEnsemble_L2 -38.245361       1.136365  31.659730   
 3         LightGBM_BAG_L1 -38.245361       1.136365  31.409857   
 4         LightGBM_BAG_L2 -38.912343       1.497464  36.941634   
 5  RandomForestMSE_BAG_L2 -39.092819       1.857261  40.986941   
 6   KNeighborsDist_BAG_L1 -72.135046       0.112256   0.061965   
 7   KNeighborsUnif_BAG_L1 -72.685921       0.111923   0.059965   
 
    pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  \
 0                0.000999           0.223883            3       True   
 1                0.455793          11.735239            2       True   
 2                0.000000           0.249873            2       True   
 3                1.136365          31.409857            1       True   
 4                0.136920           5.409848            2       True   
 5                0.496718           9.455155            2       True   
 6                0.112256           0.061965            1       True   
 7                0.111923           0.059965            1       True   
 
    fit_order  
 0          8  
 1          5  
 2          4  
 3          3  
 4          6  
 5          7  
 6          2  
 7          1  }

# Remember to set all negative values to zero
predictions_advancedV1 = predictor_new_features_advnced1.predict(test)
predictions_advancedV1[predictions_advancedV1<0] = 0

# Same submitting predictions
submission_new_features = pd.read_csv('sampleSubmission.csv')
submission_new_features["count"] = predictions_advancedV1
submission_new_features.to_csv("submission_new_features_Advanced_V1.csv", index=False)

!kaggle competitions submit -c bike-sharing-demand -f submission_new_features_Advanced_V1.csv -m "new features Advanced V1 "

Successfully submitted to Bike Sharing Demand

c:\users\hp\anaconda3\envs\aws  deep learning\lib\site-packages\cryptography\hazmat\backends\openssl\x509.py:17: CryptographyDeprecationWarning: This version of cryptography contains a temporary pyOpenSSL fallback path. Upgrade pyOpenSSL now.
  utils.DeprecatedIn35,

  0%|          | 0.00/194k [00:00<?, ?B/s]
  4%|4         | 8.00k/194k [00:00<00:03, 62.9kB/s]
 53%|#####3    | 104k/194k [00:00<00:00, 484kB/s]  
100%|##########| 194k/194k [00:00<00:00, 644kB/s]
100%|##########| 194k/194k [00:01<00:00, 103kB/s]

!kaggle competitions submissions -c bike-sharing-demand

fileName                                 date                 description                                   status    publicScore  privateScore  
---------------------------------------  -------------------  --------------------------------------------  --------  -----------  ------------  
submission_new_features_Advanced_V1.csv  2021-12-08 12:20:43  new features Advanced V1                      complete  0.45810      0.45810       
submission_new_features_Advanced_V1.csv  2021-12-08 12:20:12  new features                                  complete  0.45810      0.45810       
submission_new_features_log.csv          2021-12-08 12:07:08  NF - Model 2  (Basic  Log Transform )         complete  0.50789      0.50789       
submission_new_features_log.csv          2021-12-08 12:06:06  NF - Model 2  (Basic  Log Transform )         complete  0.50674      0.50674       
submission_new_features_log.csv          2021-12-08 11:34:53  NF - Model 2  (Basic  Log Transform )         complete  0.50674      0.50674       
submission_new_features.csv              2021-12-08 11:24:55  NF - Model 1  (Basic  Time Catogricla data )  complete  0.50674      0.50674       
submission.csv                           2021-12-08 11:13:27  first raw submission                          complete  1.38952      1.38952       
submission_new_hpo.csv                   2021-12-08 11:06:33  new features with hyperparameters Tune        complete  0.48748      0.48748       
submission_new_hpo.csv                   2021-12-08 09:14:10  new features with hyperparameters Tune        complete  0.51858      0.51858       
submission_new_hpo_ensV4.csv             2021-12-07 15:02:09  new features with hyperparameters Vקרדןםמ 4   complete  0.48961      0.48961       
submission_new_hpo.csv                   2021-12-07 14:29:38  new features with hyperparameters Tune        complete  0.48338      0.48338       
submission_new_featuresV3.csv            2021-12-07 14:00:06  new featuresV3                                complete  0.45074      0.45074       
submission_new_featuresV2.csv            2021-12-07 13:39:45  new features                                  complete  0.50306      0.50306       
submission_new_features.csv              2021-12-07 13:09:48  new features                                  complete  0.51097      0.51097       
submission.csv                           2021-12-07 13:04:23  first raw submission                          complete  1.39309      1.39309       
submission_new_hpo.csv                   2021-12-07 11:12:13  new features with hyperparameters Tune        complete  0.45509      0.45509       
submission_new_featuresV3.csv            2021-12-07 10:53:43  new featuresV3                                complete  0.45057      0.45057       
submission_new_featuresV2.csv            2021-12-07 10:43:05  new features                                  complete  0.47595      0.47595       
submission_new_featuresV2.csv            2021-12-07 10:42:32  new features                                  error     None         None          
submission_new_features.csv              2021-12-07 10:32:26  new features                                  complete  0.50670      0.50670       
submission.csv                           2021-12-07 10:24:27  first raw submission                          complete  1.38989      1.38989       
submission_new_hpo.csv                   2021-12-07 09:08:34  new features with hyperparameters             complete  0.46172      0.46172       
submission_new_hpo.csv                   2021-12-07 08:59:07  new features with hyperparameters             complete  0.45760      0.45760       
submission_new_features.csv              2021-12-06 20:57:01  new features                                  complete  0.44755      0.44755       
submission_new_features.csv              2021-12-06 20:50:18  new features                                  complete  0.44577      0.44577       
submission_new_features.csv              2021-12-06 20:45:43  new features                                  complete  0.44592      0.44592       
submission_RMSLE.csv                     2021-12-06 17:57:57  Custome Eval Metric                           complete  0.46093      0.46093       
submission_RMSLE.csv                     2021-12-06 17:46:31  Custome Eval Metric                           complete  0.46093      0.46093       
submission_RMSLE.csv                     2021-12-06 17:25:04  Custome Eval Metric                           complete  0.46093      0.46093       
submission_RMSLE.csv                     2021-12-06 17:13:44  Custome Eval Metric                           complete  0.46093      0.46093       
submission_new_hpo_ens.csv               2021-12-06 15:59:53  new features with hyperparameters             complete  0.44685      0.44685       
submission_new_hpo_ens.csv               2021-12-06 15:55:19  new features with hyperparameters             complete  0.47243      0.47243       
submission_new_hpo_ens.csv               2021-12-06 15:14:56  new features with hyperparameters             complete  0.44685      0.44685       
submission_new_hpo_ens.csv               2021-12-06 15:11:35  new features with hyperparameters             complete  0.47219      0.47219       
submission_new_hpo_ens.csv               2021-12-06 15:03:25  new features with hyperparameters             complete  0.44685      0.44685       
submission_new_hpo.csv                   2021-12-06 15:02:29  new features with hyperparameters             complete  0.44685      0.44685       
submission_new_hpo.csv                   2021-12-06 13:32:40  new features with hyperparameters             complete  0.44575      0.44575       
submission_new_hpo.csv                   2021-12-06 12:22:42  new features with hyperparameters             complete  0.45943      0.45943       
submission_new_hpo.csv                   2021-12-06 12:19:40  new features with hyperparameters             complete  0.45999      0.45999       
submission_new_hpo.csv                   2021-12-06 11:38:43  new features with hyperparameters             complete  0.49236      0.49236       
submission_new_hpo.csv                   2021-12-06 11:23:09  new features with hyperparameters             complete  0.44575      0.44575       
submission_new_hpo.csv                   2021-12-06 11:18:36  new features with hyperparameters             complete  0.44575      0.44575       
submission_new_hpo.csv                   2021-12-06 11:13:58  new features with hyperparameters             complete  0.46954      0.46954       
submission_new_features.csv              2021-12-06 10:58:08  new features                                  complete  0.44585      0.44585       
submission_new_hpo.csv                   2021-12-06 10:54:45  new features with hyperparameters             complete  0.48625      0.48625       
submission_new_hpo.csv                   2021-12-06 10:39:14  new features with hyperparameters             complete  0.49986      0.49986       
submission_new_hpo.csv                   2021-12-06 10:35:11  new features with hyperparameters             complete  0.45109      0.45109       
submission_new_features.csv              2021-12-06 09:55:51  new features                                  complete  0.44583      0.44583       
submission_new_features.csv              2021-12-04 11:36:34  new features                                  complete  0.47874      0.47874       
submission_new_features.csv              2021-12-04 11:32:02  new features                                  complete  0.50538      0.50538

c:\users\hp\anaconda3\envs\aws  deep learning\lib\site-packages\cryptography\hazmat\backends\openssl\x509.py:17: CryptographyDeprecationWarning: This version of cryptography contains a temporary pyOpenSSL fallback path. Upgrade pyOpenSSL now.
  utils.DeprecatedIn35,

raw.head()

More Time Features¶

#Shift features (Houres )
raw.index = raw.datetime
raw["shift_1h"] = raw['count'].shift(1, freq='1H')
raw["shift_2h"] = raw['count'].shift(1, freq='2H')
raw["shift_3h"] = raw['count'].shift(1, freq='3H')
raw["shift_4h"] = raw['count'].shift(1, freq='4H')
raw["shift_5h"] = raw['count'].shift(1, freq='5H')

#calculate Rolling window mean and STD over few of the  shift columns 
raw["rollin_7d_mean"] = raw['shift_1'].rolling(7).mean()
raw["rollin_7d_std"] = raw['shift_1'].rolling(7).std()
raw["rollin_30d_mean"] = raw['shift_1'].rolling(30).mean()
raw["rollin_30d_std"] = raw['shift_1'].rolling(30).std()
raw["rollin_12h_mean"] = raw['shift_1h'].rolling(12).mean()
raw["rollind_12h_std"] = raw['shift_1h'].rolling(12).std()
raw["rollin_24h_mean"] = raw['shift_1h'].rolling(24).mean()
raw["rollind_24h_std"] = raw['shift_1h'].rolling(24).std()

#Build the model with the new Features 
raw.reset_index(inplace=True,drop=True)
train = raw[raw['data']=='train'].copy(deep=True)
test = raw[raw['data']=='test'].copy(deep=True)
test.reset_index(inplace=True,drop=True)
train.drop(['data'],axis=1,inplace =True)
test.drop(['data'],axis=1,inplace =True)

categorical_features = ["hour","day","month","season","weather","holiday","workingday","weekday",'holiday', 'workingday']
for items in categorical_features:
    train[items] = train[items].astype("category")
    test[items] = test[items].astype("category")

train.head()

predictor_new_features_advanced_V3 = TabularPredictor(label="count",eval_metric='root_mean_squared_error').fit(
    train_data=train,
    time_limit=60,
    presets="best_quality"
)

No path specified. Models will be saved in: "AutogluonModels/ag-20211210_084351\"
Presets specified: ['best_quality']
Beginning AutoGluon training ... Time limit = 60s
AutoGluon will save models to "AutogluonModels/ag-20211210_084351\"
AutoGluon Version:  0.3.1
Train Data Rows:    10886
Train Data Columns: 31
Preprocessing data ...
AutoGluon infers your prediction problem is: 'regression' (because dtype of label-column == float and many unique label-values observed).
	Label info (max, min, mean, stddev): (977.0, 1.0, 191.57413, 181.14445)
	If 'regression' is not the correct problem_type, please manually specify the problem_type argument in fit() (You may specify problem_type as one of: ['binary', 'multiclass', 'regression'])
Using Feature Generators to preprocess the data ...
Fitting AutoMLPipelineFeatureGenerator...
	Available Memory:                    2299.58 MB
	Train Data (Original)  Memory Usage: 2.09 MB (0.1% of available memory)
	Inferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.
	Stage 1 Generators:
		Fitting AsTypeFeatureGenerator...
			Note: Converting 2 features to boolean dtype as they only contain 2 unique values.
	Stage 2 Generators:
		Fitting FillNaFeatureGenerator...
	Stage 3 Generators:
		Fitting IdentityFeatureGenerator...
		Fitting CategoryFeatureGenerator...
			Fitting CategoryMemoryMinimizeFeatureGenerator...
		Fitting DatetimeFeatureGenerator...
	Stage 4 Generators:
		Fitting DropUniqueFeatureGenerator...
	Types of features in original data (raw dtype, special dtypes):
		('category', []) :  8 | ['season', 'holiday', 'workingday', 'weather', 'hour', ...]
		('datetime', []) :  2 | ['datetime', 'date']
		('float', [])    : 20 | ['temp', 'atemp', 'windspeed', 'shift_1', 'shift_2', ...]
		('int', [])      :  1 | ['humidity']
	Types of features in processed data (raw dtype, special dtypes):
		('category', [])             :  6 | ['season', 'weather', 'hour', 'weekday', 'day', ...]
		('float', [])                : 20 | ['temp', 'atemp', 'windspeed', 'shift_1', 'shift_2', ...]
		('int', [])                  :  1 | ['humidity']
		('int', ['bool'])            :  2 | ['holiday', 'workingday']
		('int', ['datetime_as_int']) :  2 | ['datetime', 'date']
	0.2s = Fit runtime
	31 features in original data used to generate 31 features in processed data.
	Train Data (Processed) Memory Usage: 2.09 MB (0.1% of available memory)
Data preprocessing and feature engineering runtime = 0.27s ...
AutoGluon will gauge predictive performance using evaluation metric: 'root_mean_squared_error'
	To change this, specify the eval_metric argument of fit()
AutoGluon will fit 2 stack levels (L1 to L2) ...
Fitting 11 L1 models ...
Fitting model: KNeighborsUnif_BAG_L1 ... Training model for up to 39.81s of the 59.73s of remaining time.
	-48.3158	 = Validation score   (root_mean_squared_error)
	0.02s	 = Training   runtime
	4.59s	 = Validation runtime
Fitting model: KNeighborsDist_BAG_L1 ... Training model for up to 34.99s of the 54.9s of remaining time.
	-47.6509	 = Validation score   (root_mean_squared_error)
	0.02s	 = Training   runtime
	5.14s	 = Validation runtime
Fitting model: LightGBMXT_BAG_L1 ... Training model for up to 29.61s of the 49.53s of remaining time.
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	Ran out of time, early stopping on iteration 908. Best iteration is:
	[833]	train_set's rmse: 16.3415	valid_set's rmse: 30.9647
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "

[1000]	train_set's rmse: 14.7202	valid_set's rmse: 34.6147

C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "

[1000]	train_set's rmse: 14.7621	valid_set's rmse: 30.8732

C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	-31.9404	 = Validation score   (root_mean_squared_error)
	16.02s	 = Training   runtime
	0.34s	 = Validation runtime
Fitting model: LightGBM_BAG_L1 ... Training model for up to 12.54s of the 32.46s of remaining time.
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	Ran out of time, early stopping on iteration 325. Best iteration is:
	[268]	train_set's rmse: 20.314	valid_set's rmse: 32.4412
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	Ran out of time, early stopping on iteration 308. Best iteration is:
	[299]	train_set's rmse: 19.1567	valid_set's rmse: 35.8671
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	Ran out of time, early stopping on iteration 415. Best iteration is:
	[415]	train_set's rmse: 16.7362	valid_set's rmse: 31.1802
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	Ran out of time, early stopping on iteration 411. Best iteration is:
	[374]	train_set's rmse: 17.4503	valid_set's rmse: 32.9475
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	Ran out of time, early stopping on iteration 393. Best iteration is:
	[270]	train_set's rmse: 20.0538	valid_set's rmse: 34.704
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	Ran out of time, early stopping on iteration 580. Best iteration is:
	[579]	train_set's rmse: 13.9387	valid_set's rmse: 30.9876
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	Ran out of time, early stopping on iteration 607. Best iteration is:
	[572]	train_set's rmse: 14.1132	valid_set's rmse: 31.8345
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	-33.6946	 = Validation score   (root_mean_squared_error)
	11.01s	 = Training   runtime
	0.22s	 = Validation runtime
Fitting model: RandomForestMSE_BAG_L1 ... Training model for up to 1.04s of the 20.96s of remaining time.
	-40.2536	 = Validation score   (root_mean_squared_error)
	13.02s	 = Training   runtime
	0.8s	 = Validation runtime
Completed 1/20 k-fold bagging repeats ...
Fitting model: WeightedEnsemble_L2 ... Training model for up to 59.73s of the 5.52s of remaining time.
	-31.7567	 = Validation score   (root_mean_squared_error)
	0.32s	 = Training   runtime
	0.0s	 = Validation runtime
Fitting 9 L2 models ...
Fitting model: LightGBMXT_BAG_L2 ... Training model for up to 5.18s of the 5.16s of remaining time.
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	Ran out of time, early stopping on iteration 74. Best iteration is:
	[74]	train_set's rmse: 31.6215	valid_set's rmse: 35.3579
	Time limit exceeded... Skipping LightGBMXT_BAG_L2.
Fitting model: LightGBM_BAG_L2 ... Training model for up to 4.61s of the 4.6s of remaining time.
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	Ran out of time, early stopping on iteration 58. Best iteration is:
	[58]	train_set's rmse: 29.5088	valid_set's rmse: 34.3943
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	Ran out of time, early stopping on iteration 68. Best iteration is:
	[68]	train_set's rmse: 28.0471	valid_set's rmse: 33.1249
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	Ran out of time, early stopping on iteration 95. Best iteration is:
	[86]	train_set's rmse: 26.3968	valid_set's rmse: 33.2001
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	Ran out of time, early stopping on iteration 74. Best iteration is:
	[74]	train_set's rmse: 27.6488	valid_set's rmse: 29.5122
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	Ran out of time, early stopping on iteration 74. Best iteration is:
	[74]	train_set's rmse: 27.6217	valid_set's rmse: 31.3089
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	Ran out of time, early stopping on iteration 90. Best iteration is:
	[83]	train_set's rmse: 26.6773	valid_set's rmse: 32.5055
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	Ran out of time, early stopping on iteration 56. Best iteration is:
	[56]	train_set's rmse: 30.2334	valid_set's rmse: 31.1786
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	Ran out of time, early stopping on iteration 50. Best iteration is:
	[50]	train_set's rmse: 31.6316	valid_set's rmse: 37.014
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	Ran out of time, early stopping on iteration 95. Best iteration is:
	[81]	train_set's rmse: 26.8663	valid_set's rmse: 33.5921
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	Ran out of time, early stopping on iteration 76. Best iteration is:
	[76]	train_set's rmse: 27.2696	valid_set's rmse: 32.6982
	-32.9087	 = Validation score   (root_mean_squared_error)
	4.34s	 = Training   runtime
	0.15s	 = Validation runtime
Fitting model: RandomForestMSE_BAG_L2 ... Training model for up to 0.01s of the -0.0s of remaining time.
	-33.0039	 = Validation score   (root_mean_squared_error)
	18.66s	 = Training   runtime
	0.8s	 = Validation runtime
Completed 1/20 k-fold bagging repeats ...
Fitting model: WeightedEnsemble_L3 ... Training model for up to 59.73s of the -20.85s of remaining time.
	-32.4898	 = Validation score   (root_mean_squared_error)
	0.16s	 = Training   runtime
	0.0s	 = Validation runtime
AutoGluon training complete, total runtime = 81.04s ...
TabularPredictor saved. To load, use: predictor = TabularPredictor.load("AutogluonModels/ag-20211210_084351\")

predictor_new_features_advanced_V3.fit_summary()

*** Summary of fit() ***
Estimated performance of each model:
                    model  score_val  pred_time_val   fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order
0     WeightedEnsemble_L2 -31.636736       5.734774  27.997924                0.001013           0.308821            2       True          6
1       LightGBMXT_BAG_L1 -31.714531       0.477777  18.554368                0.477777          18.554368            1       True          3
2       LightGBMXT_BAG_L2 -33.398709      11.105198  45.245967                0.175928           4.644301            2       True          7
3     WeightedEnsemble_L3 -33.398709      11.106197  45.247964                0.000999           0.001998            3       True          8
4         LightGBM_BAG_L1 -34.422494       0.235880   9.107751                0.235880           9.107751            1       True          4
5  RandomForestMSE_BAG_L1 -40.409168       0.534705  12.890577                0.534705          12.890577            1       True          5
6   KNeighborsDist_BAG_L1 -47.578748       5.020104   0.026984                5.020104           0.026984            1       True          2
7   KNeighborsUnif_BAG_L1 -48.240770       4.660803   0.021986                4.660803           0.021986            1       True          1
Number of models trained: 8
Types of models trained:
{'StackerEnsembleModel_RF', 'StackerEnsembleModel_LGB', 'StackerEnsembleModel_KNN', 'WeightedEnsembleModel'}
Bagging used: True  (with 10 folds)
Multi-layer stack-ensembling used: True  (with 3 levels)
Feature Metadata (Processed):
(raw dtype, special dtypes):
('category', [])             :  6 | ['season', 'weather', 'hour', 'weekday', 'day', ...]
('float', [])                : 20 | ['temp', 'atemp', 'windspeed', 'shift_1', 'shift_2', ...]
('int', [])                  :  1 | ['humidity']
('int', ['bool'])            :  2 | ['holiday', 'workingday']
('int', ['datetime_as_int']) :  2 | ['datetime', 'date']
Plot summary of models saved to file: AutogluonModels/ag-20211208_123837\SummaryOfModels.html
*** End of fit() summary ***

{'model_types': {'KNeighborsUnif_BAG_L1': 'StackerEnsembleModel_KNN',
  'KNeighborsDist_BAG_L1': 'StackerEnsembleModel_KNN',
  'LightGBMXT_BAG_L1': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1': 'StackerEnsembleModel_LGB',
  'RandomForestMSE_BAG_L1': 'StackerEnsembleModel_RF',
  'WeightedEnsemble_L2': 'WeightedEnsembleModel',
  'LightGBMXT_BAG_L2': 'StackerEnsembleModel_LGB',
  'WeightedEnsemble_L3': 'WeightedEnsembleModel'},
 'model_performance': {'KNeighborsUnif_BAG_L1': -48.24077026076795,
  'KNeighborsDist_BAG_L1': -47.57874791446982,
  'LightGBMXT_BAG_L1': -31.714530723715097,
  'LightGBM_BAG_L1': -34.422494219802275,
  'RandomForestMSE_BAG_L1': -40.409168288443695,
  'WeightedEnsemble_L2': -31.636735889555165,
  'LightGBMXT_BAG_L2': -33.39870868524447,
  'WeightedEnsemble_L3': -33.39870868524447},
 'model_best': 'WeightedEnsemble_L2',
 'model_paths': {'KNeighborsUnif_BAG_L1': 'AutogluonModels/ag-20211208_123837\\models\\KNeighborsUnif_BAG_L1\\',
  'KNeighborsDist_BAG_L1': 'AutogluonModels/ag-20211208_123837\\models\\KNeighborsDist_BAG_L1\\',
  'LightGBMXT_BAG_L1': 'AutogluonModels/ag-20211208_123837\\models\\LightGBMXT_BAG_L1\\',
  'LightGBM_BAG_L1': 'AutogluonModels/ag-20211208_123837\\models\\LightGBM_BAG_L1\\',
  'RandomForestMSE_BAG_L1': 'AutogluonModels/ag-20211208_123837\\models\\RandomForestMSE_BAG_L1\\',
  'WeightedEnsemble_L2': 'AutogluonModels/ag-20211208_123837\\models\\WeightedEnsemble_L2\\',
  'LightGBMXT_BAG_L2': 'AutogluonModels/ag-20211208_123837\\models\\LightGBMXT_BAG_L2\\',
  'WeightedEnsemble_L3': 'AutogluonModels/ag-20211208_123837\\models\\WeightedEnsemble_L3\\'},
 'model_fit_times': {'KNeighborsUnif_BAG_L1': 0.021985769271850586,
  'KNeighborsDist_BAG_L1': 0.026984453201293945,
  'LightGBMXT_BAG_L1': 18.554367542266846,
  'LightGBM_BAG_L1': 9.107750654220581,
  'RandomForestMSE_BAG_L1': 12.890576839447021,
  'WeightedEnsemble_L2': 0.308821439743042,
  'LightGBMXT_BAG_L2': 4.644301414489746,
  'WeightedEnsemble_L3': 0.001997709274291992},
 'model_pred_times': {'KNeighborsUnif_BAG_L1': 4.660802602767944,
  'KNeighborsDist_BAG_L1': 5.02010440826416,
  'LightGBMXT_BAG_L1': 0.47777724266052246,
  'LightGBM_BAG_L1': 0.23587989807128906,
  'RandomForestMSE_BAG_L1': 0.5347051620483398,
  'WeightedEnsemble_L2': 0.001012563705444336,
  'LightGBMXT_BAG_L2': 0.17592835426330566,
  'WeightedEnsemble_L3': 0.00099945068359375},
 'num_bag_folds': 10,
 'max_stack_level': 3,
 'model_hyperparams': {'KNeighborsUnif_BAG_L1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True,
   'use_child_oof': True},
  'KNeighborsDist_BAG_L1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True,
   'use_child_oof': True},
  'LightGBMXT_BAG_L1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'RandomForestMSE_BAG_L1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True,
   'use_child_oof': True},
  'WeightedEnsemble_L2': {'use_orig_features': False,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBMXT_BAG_L2': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'WeightedEnsemble_L3': {'use_orig_features': False,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True}},
 'leaderboard':                     model  score_val  pred_time_val   fit_time  \
 0     WeightedEnsemble_L2 -31.636736       5.734774  27.997924   
 1       LightGBMXT_BAG_L1 -31.714531       0.477777  18.554368   
 2       LightGBMXT_BAG_L2 -33.398709      11.105198  45.245967   
 3     WeightedEnsemble_L3 -33.398709      11.106197  45.247964   
 4         LightGBM_BAG_L1 -34.422494       0.235880   9.107751   
 5  RandomForestMSE_BAG_L1 -40.409168       0.534705  12.890577   
 6   KNeighborsDist_BAG_L1 -47.578748       5.020104   0.026984   
 7   KNeighborsUnif_BAG_L1 -48.240770       4.660803   0.021986   
 
    pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  \
 0                0.001013           0.308821            2       True   
 1                0.477777          18.554368            1       True   
 2                0.175928           4.644301            2       True   
 3                0.000999           0.001998            3       True   
 4                0.235880           9.107751            1       True   
 5                0.534705          12.890577            1       True   
 6                5.020104           0.026984            1       True   
 7                4.660803           0.021986            1       True   
 
    fit_order  
 0          6  
 1          3  
 2          7  
 3          8  
 4          4  
 5          5  
 6          2  
 7          1  }

predictor_new_features_advanced_V3.leaderboard(train, silent=True)

features_importance = predictor_new_features_advanced_V3.feature_importance(data=train)

Computing feature importance via permutation shuffling for 31 features using 1000 rows with 3 shuffle sets...
	124.45s	= Expected runtime (41.48s per shuffle set)
	74.71s	= Actual runtime (Completed 3 of 3 shuffle sets)

# Remember to set all negative values to zero
predictionsV3 = predictor_new_features_advanced_V3.predict(test)
predictionsV3[predictionsV3<0] = 0

# Same submitting predictions
submission_new_features = pd.read_csv('sampleSubmission.csv')
submission_new_features["count"] = predictionsV3
submission_new_features.to_csv("submission_new_featuresV3.csv", index=False)

!kaggle competitions submit -c bike-sharing-demand -f submission_new_featuresV3.csv -m "new features_AdvancedV3"

Successfully submitted to Bike Sharing Demand

c:\users\hp\anaconda3\envs\aws  deep learning\lib\site-packages\cryptography\hazmat\backends\openssl\x509.py:17: CryptographyDeprecationWarning: This version of cryptography contains a temporary pyOpenSSL fallback path. Upgrade pyOpenSSL now.
  utils.DeprecatedIn35,

  0%|          | 0.00/194k [00:00<?, ?B/s]
  4%|4         | 8.00k/194k [00:00<00:03, 52.8kB/s]
 49%|####9     | 96.0k/194k [00:00<00:00, 447kB/s] 
100%|##########| 194k/194k [00:00<00:00, 663kB/s] 
100%|##########| 194k/194k [00:01<00:00, 109kB/s]

!kaggle competitions submissions -c bike-sharing-demand

fileName                                 date                 description                                   status    publicScore  privateScore  
---------------------------------------  -------------------  --------------------------------------------  --------  -----------  ------------  
submission_new_featuresV3.csv            2021-12-08 12:44:41  new features_AdvancedV3                       complete  0.45274      0.45274       
submission_new_features_Advanced_V1.csv  2021-12-08 12:20:43  new features Advanced V1                      complete  0.45810      0.45810       
submission_new_features_Advanced_V1.csv  2021-12-08 12:20:12  new features                                  complete  0.45810      0.45810       
submission_new_features_log.csv          2021-12-08 12:07:08  NF - Model 2  (Basic  Log Transform )         complete  0.50789      0.50789       
submission_new_features_log.csv          2021-12-08 12:06:06  NF - Model 2  (Basic  Log Transform )         complete  0.50674      0.50674       
submission_new_features_log.csv          2021-12-08 11:34:53  NF - Model 2  (Basic  Log Transform )         complete  0.50674      0.50674       
submission_new_features.csv              2021-12-08 11:24:55  NF - Model 1  (Basic  Time Catogricla data )  complete  0.50674      0.50674       
submission.csv                           2021-12-08 11:13:27  first raw submission                          complete  1.38952      1.38952       
submission_new_hpo.csv                   2021-12-08 11:06:33  new features with hyperparameters Tune        complete  0.48748      0.48748       
submission_new_hpo.csv                   2021-12-08 09:14:10  new features with hyperparameters Tune        complete  0.51858      0.51858       
submission_new_hpo_ensV4.csv             2021-12-07 15:02:09  new features with hyperparameters Vקרדןםמ 4   complete  0.48961      0.48961       
submission_new_hpo.csv                   2021-12-07 14:29:38  new features with hyperparameters Tune        complete  0.48338      0.48338       
submission_new_featuresV3.csv            2021-12-07 14:00:06  new featuresV3                                complete  0.45074      0.45074       
submission_new_featuresV2.csv            2021-12-07 13:39:45  new features                                  complete  0.50306      0.50306       
submission_new_features.csv              2021-12-07 13:09:48  new features                                  complete  0.51097      0.51097       
submission.csv                           2021-12-07 13:04:23  first raw submission                          complete  1.39309      1.39309       
submission_new_hpo.csv                   2021-12-07 11:12:13  new features with hyperparameters Tune        complete  0.45509      0.45509       
submission_new_featuresV3.csv            2021-12-07 10:53:43  new featuresV3                                complete  0.45057      0.45057       
submission_new_featuresV2.csv            2021-12-07 10:43:05  new features                                  complete  0.47595      0.47595       
submission_new_featuresV2.csv            2021-12-07 10:42:32  new features                                  error     None         None          
submission_new_features.csv              2021-12-07 10:32:26  new features                                  complete  0.50670      0.50670       
submission.csv                           2021-12-07 10:24:27  first raw submission                          complete  1.38989      1.38989       
submission_new_hpo.csv                   2021-12-07 09:08:34  new features with hyperparameters             complete  0.46172      0.46172       
submission_new_hpo.csv                   2021-12-07 08:59:07  new features with hyperparameters             complete  0.45760      0.45760       
submission_new_features.csv              2021-12-06 20:57:01  new features                                  complete  0.44755      0.44755       
submission_new_features.csv              2021-12-06 20:50:18  new features                                  complete  0.44577      0.44577       
submission_new_features.csv              2021-12-06 20:45:43  new features                                  complete  0.44592      0.44592       
submission_RMSLE.csv                     2021-12-06 17:57:57  Custome Eval Metric                           complete  0.46093      0.46093       
submission_RMSLE.csv                     2021-12-06 17:46:31  Custome Eval Metric                           complete  0.46093      0.46093       
submission_RMSLE.csv                     2021-12-06 17:25:04  Custome Eval Metric                           complete  0.46093      0.46093       
submission_RMSLE.csv                     2021-12-06 17:13:44  Custome Eval Metric                           complete  0.46093      0.46093       
submission_new_hpo_ens.csv               2021-12-06 15:59:53  new features with hyperparameters             complete  0.44685      0.44685       
submission_new_hpo_ens.csv               2021-12-06 15:55:19  new features with hyperparameters             complete  0.47243      0.47243       
submission_new_hpo_ens.csv               2021-12-06 15:14:56  new features with hyperparameters             complete  0.44685      0.44685       
submission_new_hpo_ens.csv               2021-12-06 15:11:35  new features with hyperparameters             complete  0.47219      0.47219       
submission_new_hpo_ens.csv               2021-12-06 15:03:25  new features with hyperparameters             complete  0.44685      0.44685       
submission_new_hpo.csv                   2021-12-06 15:02:29  new features with hyperparameters             complete  0.44685      0.44685       
submission_new_hpo.csv                   2021-12-06 13:32:40  new features with hyperparameters             complete  0.44575      0.44575       
submission_new_hpo.csv                   2021-12-06 12:22:42  new features with hyperparameters             complete  0.45943      0.45943       
submission_new_hpo.csv                   2021-12-06 12:19:40  new features with hyperparameters             complete  0.45999      0.45999       
submission_new_hpo.csv                   2021-12-06 11:38:43  new features with hyperparameters             complete  0.49236      0.49236       
submission_new_hpo.csv                   2021-12-06 11:23:09  new features with hyperparameters             complete  0.44575      0.44575       
submission_new_hpo.csv                   2021-12-06 11:18:36  new features with hyperparameters             complete  0.44575      0.44575       
submission_new_hpo.csv                   2021-12-06 11:13:58  new features with hyperparameters             complete  0.46954      0.46954       
submission_new_features.csv              2021-12-06 10:58:08  new features                                  complete  0.44585      0.44585       
submission_new_hpo.csv                   2021-12-06 10:54:45  new features with hyperparameters             complete  0.48625      0.48625       
submission_new_hpo.csv                   2021-12-06 10:39:14  new features with hyperparameters             complete  0.49986      0.49986       
submission_new_hpo.csv                   2021-12-06 10:35:11  new features with hyperparameters             complete  0.45109      0.45109       
submission_new_features.csv              2021-12-06 09:55:51  new features                                  complete  0.44583      0.44583       
submission_new_features.csv              2021-12-04 11:36:34  new features                                  complete  0.47874      0.47874

c:\users\hp\anaconda3\envs\aws  deep learning\lib\site-packages\cryptography\hazmat\backends\openssl\x509.py:17: CryptographyDeprecationWarning: This version of cryptography contains a temporary pyOpenSSL fallback path. Upgrade pyOpenSSL now.
  utils.DeprecatedIn35,

features_importance
features_importance.index[0:25]

Index(['shift_1h', 'hour', 'shift_7', 'workingday', 'shift_1', 'weather',
       'weekday', 'rollind_24h_std', 'day', 'humidity', 'month', 'shift_5h',
       'shift_4h', 'shift_2h', 'shift_3h', 'temp', 'datetime',
       'rollind_12h_std', 'atemp', 'rollin_12h_mean', 'shift_30',
       'rollin_24h_mean', 'rollin_7d_std', 'shift_2', 'date'],
      dtype='object')

#Use Only the High score imortant features 
df_features = train[features_importance.index[0:25]].copy(deep=True)
df_features['count'] = train['count']
df_test = test[features_importance.index[0:25]].copy(deep=True)

predictor_new_features_important = TabularPredictor(label="count",eval_metric='root_mean_squared_error').fit(
    train_data=df_features,
    time_limit=60,
    presets="best_quality"
)

No path specified. Models will be saved in: "AutogluonModels/ag-20211208_135413\"
Presets specified: ['best_quality']
Beginning AutoGluon training ... Time limit = 60s
AutoGluon will save models to "AutogluonModels/ag-20211208_135413\"
AutoGluon Version:  0.3.1
Train Data Rows:    10886
Train Data Columns: 25
Preprocessing data ...
AutoGluon infers your prediction problem is: 'regression' (because dtype of label-column == float and many unique label-values observed).
	Label info (max, min, mean, stddev): (977.0, 1.0, 191.57413, 181.14445)
	If 'regression' is not the correct problem_type, please manually specify the problem_type argument in fit() (You may specify problem_type as one of: ['binary', 'multiclass', 'regression'])
Using Feature Generators to preprocess the data ...
Fitting AutoMLPipelineFeatureGenerator...
	Available Memory:                    3992.79 MB
	Train Data (Original)  Memory Usage: 1.72 MB (0.0% of available memory)
	Inferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.
	Stage 1 Generators:
		Fitting AsTypeFeatureGenerator...
			Note: Converting 1 features to boolean dtype as they only contain 2 unique values.
	Stage 2 Generators:
		Fitting FillNaFeatureGenerator...
	Stage 3 Generators:
		Fitting IdentityFeatureGenerator...
		Fitting CategoryFeatureGenerator...
			Fitting CategoryMemoryMinimizeFeatureGenerator...
		Fitting DatetimeFeatureGenerator...
	Stage 4 Generators:
		Fitting DropUniqueFeatureGenerator...
	Types of features in original data (raw dtype, special dtypes):
		('category', []) :  6 | ['hour', 'workingday', 'weather', 'weekday', 'day', ...]
		('datetime', []) :  2 | ['datetime', 'date']
		('float', [])    : 16 | ['shift_1h', 'shift_7', 'shift_1', 'rollind_24h_std', 'shift_5h', ...]
		('int', [])      :  1 | ['humidity']
	Types of features in processed data (raw dtype, special dtypes):
		('category', [])             :  5 | ['hour', 'weather', 'weekday', 'day', 'month']
		('float', [])                : 16 | ['shift_1h', 'shift_7', 'shift_1', 'rollind_24h_std', 'shift_5h', ...]
		('int', [])                  :  1 | ['humidity']
		('int', ['bool'])            :  1 | ['workingday']
		('int', ['datetime_as_int']) :  2 | ['datetime', 'date']
	0.2s = Fit runtime
	25 features in original data used to generate 25 features in processed data.
	Train Data (Processed) Memory Usage: 1.72 MB (0.0% of available memory)
Data preprocessing and feature engineering runtime = 0.25s ...
AutoGluon will gauge predictive performance using evaluation metric: 'root_mean_squared_error'
	To change this, specify the eval_metric argument of fit()
AutoGluon will fit 2 stack levels (L1 to L2) ...
Fitting 11 L1 models ...
Fitting model: KNeighborsUnif_BAG_L1 ... Training model for up to 39.82s of the 59.74s of remaining time.
	-48.5053	 = Validation score   (root_mean_squared_error)
	0.02s	 = Training   runtime
	4.57s	 = Validation runtime
Fitting model: KNeighborsDist_BAG_L1 ... Training model for up to 35.03s of the 54.95s of remaining time.
	-47.8858	 = Validation score   (root_mean_squared_error)
	0.02s	 = Training   runtime
	4.52s	 = Validation runtime
Fitting model: LightGBMXT_BAG_L1 ... Training model for up to 30.28s of the 50.2s of remaining time.
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "

[1000]	train_set's rmse: 15.1129	valid_set's rmse: 34.6313

	Ran out of time, early stopping on iteration 1281. Best iteration is:
	[1208]	train_set's rmse: 13.6645	valid_set's rmse: 34.5781
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "

[1000]	train_set's rmse: 15.1476	valid_set's rmse: 31.3633

C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	-31.6571	 = Validation score   (root_mean_squared_error)
	17.36s	 = Training   runtime
	0.51s	 = Validation runtime
Fitting model: LightGBM_BAG_L1 ... Training model for up to 11.46s of the 31.38s of remaining time.
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	Ran out of time, early stopping on iteration 251. Best iteration is:
	[249]	train_set's rmse: 21.0548	valid_set's rmse: 32.1618
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	Ran out of time, early stopping on iteration 270. Best iteration is:
	[270]	train_set's rmse: 20.2513	valid_set's rmse: 36.473
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	Ran out of time, early stopping on iteration 271. Best iteration is:
	[271]	train_set's rmse: 20.4613	valid_set's rmse: 31.0755
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	Ran out of time, early stopping on iteration 279. Best iteration is:
	[270]	train_set's rmse: 20.2672	valid_set's rmse: 32.9987
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	Ran out of time, early stopping on iteration 290. Best iteration is:
	[280]	train_set's rmse: 20.0223	valid_set's rmse: 34.2662
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	Ran out of time, early stopping on iteration 257. Best iteration is:
	[256]	train_set's rmse: 20.521	valid_set's rmse: 39.3415
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	Ran out of time, early stopping on iteration 313. Best iteration is:
	[308]	train_set's rmse: 19.3424	valid_set's rmse: 31.3924
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	Ran out of time, early stopping on iteration 339. Best iteration is:
	[336]	train_set's rmse: 18.5373	valid_set's rmse: 31.8556
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	Ran out of time, early stopping on iteration 377. Best iteration is:
	[342]	train_set's rmse: 18.3832	valid_set's rmse: 36.3048
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	Ran out of time, early stopping on iteration 419. Best iteration is:
	[297]	train_set's rmse: 19.6046	valid_set's rmse: 32.1718
	-33.9039	 = Validation score   (root_mean_squared_error)
	10.75s	 = Training   runtime
	0.27s	 = Validation runtime
Fitting model: RandomForestMSE_BAG_L1 ... Training model for up to 0.2s of the 20.12s of remaining time.
	-40.0709	 = Validation score   (root_mean_squared_error)
	11.07s	 = Training   runtime
	0.43s	 = Validation runtime
Completed 1/20 k-fold bagging repeats ...
Fitting model: WeightedEnsemble_L2 ... Training model for up to 59.75s of the 7.3s of remaining time.
	-31.5609	 = Validation score   (root_mean_squared_error)
	0.31s	 = Training   runtime
	0.0s	 = Validation runtime
Fitting 9 L2 models ...
Fitting model: LightGBMXT_BAG_L2 ... Training model for up to 6.98s of the 6.97s of remaining time.
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	Ran out of time, early stopping on iteration 220. Best iteration is:
	[211]	train_set's rmse: 25.9032	valid_set's rmse: 33.9716
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	Ran out of time, early stopping on iteration 226. Best iteration is:
	[220]	train_set's rmse: 26.0666	valid_set's rmse: 32.566
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	Ran out of time, early stopping on iteration 232. Best iteration is:
	[195]	train_set's rmse: 26.5773	valid_set's rmse: 33.9915
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	Ran out of time, early stopping on iteration 236. Best iteration is:
	[235]	train_set's rmse: 25.7137	valid_set's rmse: 30.3967
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	Ran out of time, early stopping on iteration 235. Best iteration is:
	[195]	train_set's rmse: 26.5938	valid_set's rmse: 30.5762
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	Ran out of time, early stopping on iteration 246. Best iteration is:
	[243]	train_set's rmse: 25.1824	valid_set's rmse: 32.2322
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	Ran out of time, early stopping on iteration 227. Best iteration is:
	[227]	train_set's rmse: 25.8136	valid_set's rmse: 30.9506
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	Ran out of time, early stopping on iteration 248. Best iteration is:
	[138]	train_set's rmse: 28.0279	valid_set's rmse: 34.9272
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	Ran out of time, early stopping on iteration 215. Best iteration is:
	[214]	train_set's rmse: 25.7862	valid_set's rmse: 34.018
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	Ran out of time, early stopping on iteration 281. Best iteration is:
	[278]	train_set's rmse: 24.4693	valid_set's rmse: 32.3472
	-32.633	 = Validation score   (root_mean_squared_error)
	6.48s	 = Training   runtime
	0.24s	 = Validation runtime
Fitting model: LightGBM_BAG_L2 ... Training model for up to 0.04s of the 0.03s of remaining time.
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	Ran out of time, early stopping on iteration 1. Best iteration is:
	[1]	train_set's rmse: 172.688	valid_set's rmse: 169.43
	Time limit exceeded... Skipping LightGBM_BAG_L2.
Completed 1/20 k-fold bagging repeats ...
Fitting model: WeightedEnsemble_L3 ... Training model for up to 59.75s of the -0.24s of remaining time.
	-32.633	 = Validation score   (root_mean_squared_error)
	0.0s	 = Training   runtime
	0.0s	 = Validation runtime
AutoGluon training complete, total runtime = 60.26s ...
TabularPredictor saved. To load, use: predictor = TabularPredictor.load("AutogluonModels/ag-20211208_135413\")

predictor_new_features_important.leaderboard(df_features, silent=True)

# Remember to set all negative values to zero
predictions_import_features = predictor_new_features_important.predict(df_test)
predictions_import_features[predictions_import_features<0] = 0

# Same submitting predictions
submission_new_features = pd.read_csv('sampleSubmission.csv')
submission_new_features["count"] = predictions_import_features
submission_new_features.to_csv("submission_import_features.csv", index=False)

!kaggle competitions submit -c bike-sharing-demand -f submission_import_features.csv -m "Feature Importance"
!kaggle competitions submissions -c bike-sharing-demand

Successfully submitted to Bike Sharing Demand

c:\users\hp\anaconda3\envs\aws  deep learning\lib\site-packages\cryptography\hazmat\backends\openssl\x509.py:17: CryptographyDeprecationWarning: This version of cryptography contains a temporary pyOpenSSL fallback path. Upgrade pyOpenSSL now.
  utils.DeprecatedIn35,

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fileName                                 date                 description                                   status    publicScore  privateScore  
---------------------------------------  -------------------  --------------------------------------------  --------  -----------  ------------  
submission_import_features.csv           2021-12-08 13:55:39  Feature Importance                            complete  0.45194      0.45194       
submission_import_features.csv           2021-12-08 13:53:11  Feature Importance                            complete  0.45414      0.45414       
submission_import_features.csv           2021-12-08 13:51:11  Feature Importance                            complete  0.45749      0.45749       
submission_import_features.csv           2021-12-08 13:48:53  Feature Importance                            complete  0.45210      0.45210       
submission_import_features.csv           2021-12-08 13:44:55  Feature Importance                            complete  0.48275      0.48275       
submission_import_features.csv           2021-12-08 13:42:28  Feature Importance                            complete  0.45753      0.45753       
submission_import_features.csv           2021-12-08 13:39:01  Feature Importance                            complete  0.45939      0.45939       
submission_new_featuresV3.csv            2021-12-08 12:44:41  new features_AdvancedV3                       complete  0.45274      0.45274       
submission_new_features_Advanced_V1.csv  2021-12-08 12:20:43  new features Advanced V1                      complete  0.45810      0.45810       
submission_new_features_Advanced_V1.csv  2021-12-08 12:20:12  new features                                  complete  0.45810      0.45810       
submission_new_features_log.csv          2021-12-08 12:07:08  NF - Model 2  (Basic  Log Transform )         complete  0.50789      0.50789       
submission_new_features_log.csv          2021-12-08 12:06:06  NF - Model 2  (Basic  Log Transform )         complete  0.50674      0.50674       
submission_new_features_log.csv          2021-12-08 11:34:53  NF - Model 2  (Basic  Log Transform )         complete  0.50674      0.50674       
submission_new_features.csv              2021-12-08 11:24:55  NF - Model 1  (Basic  Time Catogricla data )  complete  0.50674      0.50674       
submission.csv                           2021-12-08 11:13:27  first raw submission                          complete  1.38952      1.38952       
submission_new_hpo.csv                   2021-12-08 11:06:33  new features with hyperparameters Tune        complete  0.48748      0.48748       
submission_new_hpo.csv                   2021-12-08 09:14:10  new features with hyperparameters Tune        complete  0.51858      0.51858       
submission_new_hpo_ensV4.csv             2021-12-07 15:02:09  new features with hyperparameters Vקרדןםמ 4   complete  0.48961      0.48961       
submission_new_hpo.csv                   2021-12-07 14:29:38  new features with hyperparameters Tune        complete  0.48338      0.48338       
submission_new_featuresV3.csv            2021-12-07 14:00:06  new featuresV3                                complete  0.45074      0.45074       
submission_new_featuresV2.csv            2021-12-07 13:39:45  new features                                  complete  0.50306      0.50306       
submission_new_features.csv              2021-12-07 13:09:48  new features                                  complete  0.51097      0.51097       
submission.csv                           2021-12-07 13:04:23  first raw submission                          complete  1.39309      1.39309       
submission_new_hpo.csv                   2021-12-07 11:12:13  new features with hyperparameters Tune        complete  0.45509      0.45509       
submission_new_featuresV3.csv            2021-12-07 10:53:43  new featuresV3                                complete  0.45057      0.45057       
submission_new_featuresV2.csv            2021-12-07 10:43:05  new features                                  complete  0.47595      0.47595       
submission_new_featuresV2.csv            2021-12-07 10:42:32  new features                                  error     None         None          
submission_new_features.csv              2021-12-07 10:32:26  new features                                  complete  0.50670      0.50670       
submission.csv                           2021-12-07 10:24:27  first raw submission                          complete  1.38989      1.38989       
submission_new_hpo.csv                   2021-12-07 09:08:34  new features with hyperparameters             complete  0.46172      0.46172       
submission_new_hpo.csv                   2021-12-07 08:59:07  new features with hyperparameters             complete  0.45760      0.45760       
submission_new_features.csv              2021-12-06 20:57:01  new features                                  complete  0.44755      0.44755       
submission_new_features.csv              2021-12-06 20:50:18  new features                                  complete  0.44577      0.44577       
submission_new_features.csv              2021-12-06 20:45:43  new features                                  complete  0.44592      0.44592       
submission_RMSLE.csv                     2021-12-06 17:57:57  Custome Eval Metric                           complete  0.46093      0.46093       
submission_RMSLE.csv                     2021-12-06 17:46:31  Custome Eval Metric                           complete  0.46093      0.46093       
submission_RMSLE.csv                     2021-12-06 17:25:04  Custome Eval Metric                           complete  0.46093      0.46093       
submission_RMSLE.csv                     2021-12-06 17:13:44  Custome Eval Metric                           complete  0.46093      0.46093       
submission_new_hpo_ens.csv               2021-12-06 15:59:53  new features with hyperparameters             complete  0.44685      0.44685       
submission_new_hpo_ens.csv               2021-12-06 15:55:19  new features with hyperparameters             complete  0.47243      0.47243       
submission_new_hpo_ens.csv               2021-12-06 15:14:56  new features with hyperparameters             complete  0.44685      0.44685       
submission_new_hpo_ens.csv               2021-12-06 15:11:35  new features with hyperparameters             complete  0.47219      0.47219       
submission_new_hpo_ens.csv               2021-12-06 15:03:25  new features with hyperparameters             complete  0.44685      0.44685       
submission_new_hpo.csv                   2021-12-06 15:02:29  new features with hyperparameters             complete  0.44685      0.44685       
submission_new_hpo.csv                   2021-12-06 13:32:40  new features with hyperparameters             complete  0.44575      0.44575       
submission_new_hpo.csv                   2021-12-06 12:22:42  new features with hyperparameters             complete  0.45943      0.45943       
submission_new_hpo.csv                   2021-12-06 12:19:40  new features with hyperparameters             complete  0.45999      0.45999       
submission_new_hpo.csv                   2021-12-06 11:38:43  new features with hyperparameters             complete  0.49236      0.49236       
submission_new_hpo.csv                   2021-12-06 11:23:09  new features with hyperparameters             complete  0.44575      0.44575       
submission_new_hpo.csv                   2021-12-06 11:18:36  new features with hyperparameters             complete  0.44575      0.44575

c:\users\hp\anaconda3\envs\aws  deep learning\lib\site-packages\cryptography\hazmat\backends\openssl\x509.py:17: CryptographyDeprecationWarning: This version of cryptography contains a temporary pyOpenSSL fallback path. Upgrade pyOpenSSL now.
  utils.DeprecatedIn35,

Step 6: Hyper parameter optimization¶

There are many options for hyper parameter optimization.
Options are to change the AutoGluon higher level parameters or the individual model hyperparameters.
The hyperparameters of the models themselves that are in AutoGluon. Those need the hyperparameter and hyperparameter_tune_kwargs arguments.

Specifying hyperparameters and tuning them¶

Using the latest Features Set

time_limit = [2*60,3*60,4*60,5*60]
index=0
results = []
for item in time_limit:

 
    predictor_time_search = TabularPredictor(label="count",eval_metric='root_mean_squared_error').fit(
        train_data=df_features,
        time_limit=item,
        holdout_frac = 0.1,
        presets="best_quality"
    )
    
    results.append(predictor_time_search.leaderboard(df_features, silent=True))

No path specified. Models will be saved in: "AutogluonModels/ag-20211211_095329\"
Presets specified: ['best_quality']
Beginning AutoGluon training ... Time limit = 120s
AutoGluon will save models to "AutogluonModels/ag-20211211_095329\"
AutoGluon Version:  0.3.1
Train Data Rows:    10886
Train Data Columns: 25
Preprocessing data ...
AutoGluon infers your prediction problem is: 'regression' (because dtype of label-column == float and many unique label-values observed).
	Label info (max, min, mean, stddev): (977.0, 1.0, 191.57413, 181.14445)
	If 'regression' is not the correct problem_type, please manually specify the problem_type argument in fit() (You may specify problem_type as one of: ['binary', 'multiclass', 'regression'])
Using Feature Generators to preprocess the data ...
Fitting AutoMLPipelineFeatureGenerator...
	Available Memory:                    4061.61 MB
	Train Data (Original)  Memory Usage: 1.72 MB (0.0% of available memory)
	Inferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.
	Stage 1 Generators:
		Fitting AsTypeFeatureGenerator...
			Note: Converting 1 features to boolean dtype as they only contain 2 unique values.
	Stage 2 Generators:
		Fitting FillNaFeatureGenerator...
	Stage 3 Generators:
		Fitting IdentityFeatureGenerator...
		Fitting CategoryFeatureGenerator...
			Fitting CategoryMemoryMinimizeFeatureGenerator...
		Fitting DatetimeFeatureGenerator...
	Stage 4 Generators:
		Fitting DropUniqueFeatureGenerator...
	Types of features in original data (raw dtype, special dtypes):
		('category', []) :  6 | ['hour', 'workingday', 'weather', 'weekday', 'day', ...]
		('datetime', []) :  2 | ['datetime', 'date']
		('float', [])    : 16 | ['shift_1h', 'shift_7', 'shift_1', 'rollind_24h_std', 'shift_5h', ...]
		('int', [])      :  1 | ['humidity']
	Types of features in processed data (raw dtype, special dtypes):
		('category', [])             :  5 | ['hour', 'weather', 'weekday', 'day', 'month']
		('float', [])                : 16 | ['shift_1h', 'shift_7', 'shift_1', 'rollind_24h_std', 'shift_5h', ...]
		('int', [])                  :  1 | ['humidity']
		('int', ['bool'])            :  1 | ['workingday']
		('int', ['datetime_as_int']) :  2 | ['datetime', 'date']
	0.2s = Fit runtime
	25 features in original data used to generate 25 features in processed data.
	Train Data (Processed) Memory Usage: 1.72 MB (0.0% of available memory)
Data preprocessing and feature engineering runtime = 0.28s ...
AutoGluon will gauge predictive performance using evaluation metric: 'root_mean_squared_error'
	To change this, specify the eval_metric argument of fit()
AutoGluon will fit 2 stack levels (L1 to L2) ...
Fitting 11 L1 models ...
Fitting model: KNeighborsUnif_BAG_L1 ... Training model for up to 79.79s of the 119.71s of remaining time.
	-48.4816	 = Validation score   (root_mean_squared_error)
	0.02s	 = Training   runtime
	4.33s	 = Validation runtime
Fitting model: KNeighborsDist_BAG_L1 ... Training model for up to 75.23s of the 115.15s of remaining time.
	-47.8773	 = Validation score   (root_mean_squared_error)
	0.02s	 = Training   runtime
	4.55s	 = Validation runtime
Fitting model: LightGBMXT_BAG_L1 ... Training model for up to 70.45s of the 110.37s of remaining time.
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "

[1000]	train_set's rmse: 15.2782	valid_set's rmse: 31.8115

C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	-31.7488	 = Validation score   (root_mean_squared_error)
	13.0s	 = Training   runtime
	0.32s	 = Validation runtime
Fitting model: LightGBM_BAG_L1 ... Training model for up to 56.57s of the 96.49s of remaining time.
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "

[1000]	train_set's rmse: 9.70754	valid_set's rmse: 31.1357

C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	-33.6714	 = Validation score   (root_mean_squared_error)
	14.44s	 = Training   runtime
	0.25s	 = Validation runtime
Fitting model: RandomForestMSE_BAG_L1 ... Training model for up to 41.47s of the 81.39s of remaining time.
	-39.9508	 = Validation score   (root_mean_squared_error)
	10.83s	 = Training   runtime
	0.39s	 = Validation runtime
Fitting model: CatBoost_BAG_L1 ... Training model for up to 29.03s of the 68.95s of remaining time.
	-47.1592	 = Validation score   (root_mean_squared_error)
	27.81s	 = Training   runtime
	0.09s	 = Validation runtime
Fitting model: ExtraTreesMSE_BAG_L1 ... Training model for up to 1.08s of the 41.0s of remaining time.
	-38.6335	 = Validation score   (root_mean_squared_error)
	3.53s	 = Training   runtime
	0.43s	 = Validation runtime
Completed 1/20 k-fold bagging repeats ...
Fitting model: WeightedEnsemble_L2 ... Training model for up to 119.72s of the 35.64s of remaining time.
	-31.6009	 = Validation score   (root_mean_squared_error)
	0.48s	 = Training   runtime
	0.0s	 = Validation runtime
Fitting 9 L2 models ...
Fitting model: LightGBMXT_BAG_L2 ... Training model for up to 35.14s of the 35.13s of remaining time.
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	-32.7157	 = Validation score   (root_mean_squared_error)
	9.0s	 = Training   runtime
	0.21s	 = Validation runtime
Fitting model: LightGBM_BAG_L2 ... Training model for up to 25.73s of the 25.72s of remaining time.
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	-32.299	 = Validation score   (root_mean_squared_error)
	6.72s	 = Training   runtime
	0.12s	 = Validation runtime
Fitting model: RandomForestMSE_BAG_L2 ... Training model for up to 18.78s of the 18.77s of remaining time.
	-32.7225	 = Validation score   (root_mean_squared_error)
	16.75s	 = Training   runtime
	0.61s	 = Validation runtime
Fitting model: CatBoost_BAG_L2 ... Training model for up to 0.03s of the 0.01s of remaining time.
	Time limit exceeded... Skipping CatBoost_BAG_L2.
Completed 1/20 k-fold bagging repeats ...
Fitting model: WeightedEnsemble_L3 ... Training model for up to 119.72s of the -0.11s of remaining time.
	-31.984	 = Validation score   (root_mean_squared_error)
	0.23s	 = Training   runtime
	0.0s	 = Validation runtime
AutoGluon training complete, total runtime = 120.37s ...
TabularPredictor saved. To load, use: predictor = TabularPredictor.load("AutogluonModels/ag-20211211_095329\")
No path specified. Models will be saved in: "AutogluonModels/ag-20211211_095547\"
Presets specified: ['best_quality']
Beginning AutoGluon training ... Time limit = 180s
AutoGluon will save models to "AutogluonModels/ag-20211211_095547\"
AutoGluon Version:  0.3.1
Train Data Rows:    10886
Train Data Columns: 25
Preprocessing data ...
AutoGluon infers your prediction problem is: 'regression' (because dtype of label-column == float and many unique label-values observed).
	Label info (max, min, mean, stddev): (977.0, 1.0, 191.57413, 181.14445)
	If 'regression' is not the correct problem_type, please manually specify the problem_type argument in fit() (You may specify problem_type as one of: ['binary', 'multiclass', 'regression'])
Using Feature Generators to preprocess the data ...
Fitting AutoMLPipelineFeatureGenerator...
	Available Memory:                    3637.41 MB
	Train Data (Original)  Memory Usage: 1.72 MB (0.0% of available memory)
	Inferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.
	Stage 1 Generators:
		Fitting AsTypeFeatureGenerator...
			Note: Converting 1 features to boolean dtype as they only contain 2 unique values.
	Stage 2 Generators:
		Fitting FillNaFeatureGenerator...
	Stage 3 Generators:
		Fitting IdentityFeatureGenerator...
		Fitting CategoryFeatureGenerator...
			Fitting CategoryMemoryMinimizeFeatureGenerator...
		Fitting DatetimeFeatureGenerator...
	Stage 4 Generators:
		Fitting DropUniqueFeatureGenerator...
	Types of features in original data (raw dtype, special dtypes):
		('category', []) :  6 | ['hour', 'workingday', 'weather', 'weekday', 'day', ...]
		('datetime', []) :  2 | ['datetime', 'date']
		('float', [])    : 16 | ['shift_1h', 'shift_7', 'shift_1', 'rollind_24h_std', 'shift_5h', ...]
		('int', [])      :  1 | ['humidity']
	Types of features in processed data (raw dtype, special dtypes):
		('category', [])             :  5 | ['hour', 'weather', 'weekday', 'day', 'month']
		('float', [])                : 16 | ['shift_1h', 'shift_7', 'shift_1', 'rollind_24h_std', 'shift_5h', ...]
		('int', [])                  :  1 | ['humidity']
		('int', ['bool'])            :  1 | ['workingday']
		('int', ['datetime_as_int']) :  2 | ['datetime', 'date']
	0.3s = Fit runtime
	25 features in original data used to generate 25 features in processed data.
	Train Data (Processed) Memory Usage: 1.72 MB (0.0% of available memory)
Data preprocessing and feature engineering runtime = 0.32s ...
AutoGluon will gauge predictive performance using evaluation metric: 'root_mean_squared_error'
	To change this, specify the eval_metric argument of fit()
AutoGluon will fit 2 stack levels (L1 to L2) ...
Fitting 11 L1 models ...
Fitting model: KNeighborsUnif_BAG_L1 ... Training model for up to 119.75s of the 179.67s of remaining time.
	-48.4816	 = Validation score   (root_mean_squared_error)
	0.02s	 = Training   runtime
	4.88s	 = Validation runtime
Fitting model: KNeighborsDist_BAG_L1 ... Training model for up to 114.63s of the 174.55s of remaining time.
	-47.8773	 = Validation score   (root_mean_squared_error)
	0.02s	 = Training   runtime
	4.93s	 = Validation runtime
Fitting model: LightGBMXT_BAG_L1 ... Training model for up to 109.47s of the 169.38s of remaining time.
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "

[1000]	train_set's rmse: 15.2782	valid_set's rmse: 31.8115

C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	-31.7488	 = Validation score   (root_mean_squared_error)
	15.79s	 = Training   runtime
	0.41s	 = Validation runtime
Fitting model: LightGBM_BAG_L1 ... Training model for up to 92.59s of the 152.51s of remaining time.
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "

[1000]	train_set's rmse: 9.70754	valid_set's rmse: 31.1357

C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	-33.6714	 = Validation score   (root_mean_squared_error)
	13.67s	 = Training   runtime
	0.24s	 = Validation runtime
Fitting model: RandomForestMSE_BAG_L1 ... Training model for up to 78.29s of the 138.21s of remaining time.
	-39.9508	 = Validation score   (root_mean_squared_error)
	11.31s	 = Training   runtime
	0.49s	 = Validation runtime
Fitting model: CatBoost_BAG_L1 ... Training model for up to 65.18s of the 125.1s of remaining time.
	Time limit exceeded... Skipping CatBoost_BAG_L1.
Completed 1/20 k-fold bagging repeats ...
Fitting model: WeightedEnsemble_L2 ... Training model for up to 179.68s of the 59.7s of remaining time.
	-31.6009	 = Validation score   (root_mean_squared_error)
	0.34s	 = Training   runtime
	0.0s	 = Validation runtime
Fitting 9 L2 models ...
Fitting model: LightGBMXT_BAG_L2 ... Training model for up to 59.34s of the 59.33s of remaining time.
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	-32.8287	 = Validation score   (root_mean_squared_error)
	9.82s	 = Training   runtime
	0.23s	 = Validation runtime
Fitting model: LightGBM_BAG_L2 ... Training model for up to 49.04s of the 49.03s of remaining time.
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	-32.2957	 = Validation score   (root_mean_squared_error)
	8.02s	 = Training   runtime
	0.13s	 = Validation runtime
Fitting model: RandomForestMSE_BAG_L2 ... Training model for up to 40.76s of the 40.75s of remaining time.
	-32.6416	 = Validation score   (root_mean_squared_error)
	16.83s	 = Training   runtime
	0.49s	 = Validation runtime
Fitting model: CatBoost_BAG_L2 ... Training model for up to 22.08s of the 22.07s of remaining time.
	-35.1461	 = Validation score   (root_mean_squared_error)
	21.39s	 = Training   runtime
	0.1s	 = Validation runtime
Fitting model: ExtraTreesMSE_BAG_L2 ... Training model for up to 0.44s of the 0.43s of remaining time.
	-32.2071	 = Validation score   (root_mean_squared_error)
	4.34s	 = Training   runtime
	0.49s	 = Validation runtime
Completed 1/20 k-fold bagging repeats ...
Fitting model: WeightedEnsemble_L3 ... Training model for up to 179.68s of the -5.81s of remaining time.
	-31.9063	 = Validation score   (root_mean_squared_error)
	0.3s	 = Training   runtime
	0.0s	 = Validation runtime
AutoGluon training complete, total runtime = 186.14s ...
TabularPredictor saved. To load, use: predictor = TabularPredictor.load("AutogluonModels/ag-20211211_095547\")
No path specified. Models will be saved in: "AutogluonModels/ag-20211211_095911\"
Presets specified: ['best_quality']
Beginning AutoGluon training ... Time limit = 240s
AutoGluon will save models to "AutogluonModels/ag-20211211_095911\"
AutoGluon Version:  0.3.1
Train Data Rows:    10886
Train Data Columns: 25
Preprocessing data ...
AutoGluon infers your prediction problem is: 'regression' (because dtype of label-column == float and many unique label-values observed).
	Label info (max, min, mean, stddev): (977.0, 1.0, 191.57413, 181.14445)
	If 'regression' is not the correct problem_type, please manually specify the problem_type argument in fit() (You may specify problem_type as one of: ['binary', 'multiclass', 'regression'])
Using Feature Generators to preprocess the data ...
Fitting AutoMLPipelineFeatureGenerator...
	Available Memory:                    3785.26 MB
	Train Data (Original)  Memory Usage: 1.72 MB (0.0% of available memory)
	Inferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.
	Stage 1 Generators:
		Fitting AsTypeFeatureGenerator...
			Note: Converting 1 features to boolean dtype as they only contain 2 unique values.
	Stage 2 Generators:
		Fitting FillNaFeatureGenerator...
	Stage 3 Generators:
		Fitting IdentityFeatureGenerator...
		Fitting CategoryFeatureGenerator...
			Fitting CategoryMemoryMinimizeFeatureGenerator...
		Fitting DatetimeFeatureGenerator...
	Stage 4 Generators:
		Fitting DropUniqueFeatureGenerator...
	Types of features in original data (raw dtype, special dtypes):
		('category', []) :  6 | ['hour', 'workingday', 'weather', 'weekday', 'day', ...]
		('datetime', []) :  2 | ['datetime', 'date']
		('float', [])    : 16 | ['shift_1h', 'shift_7', 'shift_1', 'rollind_24h_std', 'shift_5h', ...]
		('int', [])      :  1 | ['humidity']
	Types of features in processed data (raw dtype, special dtypes):
		('category', [])             :  5 | ['hour', 'weather', 'weekday', 'day', 'month']
		('float', [])                : 16 | ['shift_1h', 'shift_7', 'shift_1', 'rollind_24h_std', 'shift_5h', ...]
		('int', [])                  :  1 | ['humidity']
		('int', ['bool'])            :  1 | ['workingday']
		('int', ['datetime_as_int']) :  2 | ['datetime', 'date']
	0.2s = Fit runtime
	25 features in original data used to generate 25 features in processed data.
	Train Data (Processed) Memory Usage: 1.72 MB (0.0% of available memory)
Data preprocessing and feature engineering runtime = 0.24s ...
AutoGluon will gauge predictive performance using evaluation metric: 'root_mean_squared_error'
	To change this, specify the eval_metric argument of fit()
AutoGluon will fit 2 stack levels (L1 to L2) ...
Fitting 11 L1 models ...
Fitting model: KNeighborsUnif_BAG_L1 ... Training model for up to 159.8s of the 239.75s of remaining time.
	-48.4816	 = Validation score   (root_mean_squared_error)
	0.02s	 = Training   runtime
	4.41s	 = Validation runtime
Fitting model: KNeighborsDist_BAG_L1 ... Training model for up to 155.17s of the 235.12s of remaining time.
	-47.8773	 = Validation score   (root_mean_squared_error)
	0.03s	 = Training   runtime
	4.76s	 = Validation runtime
Fitting model: LightGBMXT_BAG_L1 ... Training model for up to 150.07s of the 230.02s of remaining time.
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "

[1000]	train_set's rmse: 15.2782	valid_set's rmse: 31.8115

for x in range(3):
    print(results[x])

                     model    score_test  score_val  pred_time_test  \
0    KNeighborsDist_BAG_L1 -2.068240e-07 -47.877308        4.836791   
1     ExtraTreesMSE_BAG_L1 -1.424137e+01 -38.633465        0.619097   
2   RandomForestMSE_BAG_L1 -1.476350e+01 -39.950767        0.706671   
3        LightGBMXT_BAG_L2 -1.548739e+01 -32.715673       16.304163   
4      WeightedEnsemble_L3 -1.592625e+01 -31.984035       17.436274   
5   RandomForestMSE_BAG_L2 -1.639805e+01 -32.722494       15.651911   
6          LightGBM_BAG_L2 -1.726698e+01 -32.298959       15.467255   
7          LightGBM_BAG_L1 -1.760263e+01 -33.671413        1.818951   
8      WeightedEnsemble_L2 -1.860298e+01 -31.600890        9.146291   
9        LightGBMXT_BAG_L1 -1.939929e+01 -31.748761        2.480554   
10   KNeighborsUnif_BAG_L1 -3.893895e+01 -48.481624        4.361507   
11         CatBoost_BAG_L1 -4.474424e+01 -47.159236        0.173953   

    pred_time_val    fit_time  pred_time_test_marginal  \
0        4.547157    0.017988                 4.836791   
1        0.433752    3.530076                 0.619097   
2        0.390791   10.831395                 0.706671   
3       10.562694   78.645502                 1.306638   
4       11.293290  102.346343                 0.007995   
5       10.965483   86.396551                 0.654386   
6       10.477743   76.369836                 0.469729   
7        0.253854   14.436683                 1.818951   
8        5.118831   27.929887                 0.009995   
9        0.316820   12.998489                 2.480554   
10       4.325527    0.023987                 4.361507   
11       0.088913   27.807089                 0.173953   

    pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  \
0                 4.547157           0.017988            1       True   
1                 0.433752           3.530076            1       True   
2                 0.390791          10.831395            1       True   
3                 0.205879           8.999795            2       True   
4                 0.000999           0.225869            3       True   
5                 0.608669          16.750844            2       True   
6                 0.120928           6.724129            2       True   
7                 0.253854          14.436683            1       True   
8                 0.001000           0.476726            2       True   
9                 0.316820          12.998489            1       True   
10                4.325527           0.023987            1       True   
11                0.088913          27.807089            1       True   

    fit_order  
0           2  
1           7  
2           5  
3           9  
4          12  
5          11  
6          10  
7           4  
8           8  
9           3  
10          1  
11          6  
                     model    score_test  score_val  pred_time_test  \
0    KNeighborsDist_BAG_L1 -2.068240e-07 -47.877308        4.513487   
1   RandomForestMSE_BAG_L1 -1.476350e+01 -39.950767        0.759706   
2        LightGBMXT_BAG_L2 -1.554346e+01 -32.828651       15.969034   
3      WeightedEnsemble_L3 -1.627839e+01 -31.906257       17.666282   
4   RandomForestMSE_BAG_L2 -1.667871e+01 -32.641605       15.347082   
5     ExtraTreesMSE_BAG_L2 -1.698762e+01 -32.207127       15.390902   
6          LightGBM_BAG_L2 -1.742097e+01 -32.295674       15.161499   
7          LightGBM_BAG_L1 -1.760263e+01 -33.671413        1.763984   
8      WeightedEnsemble_L2 -1.860298e+01 -31.600890        8.878850   
9        LightGBMXT_BAG_L1 -1.939929e+01 -31.748761        2.594505   
10         CatBoost_BAG_L2 -2.146520e+01 -35.146128       14.906253   
11   KNeighborsUnif_BAG_L1 -3.893895e+01 -48.481624        5.105061   

    pred_time_val   fit_time  pred_time_test_marginal  pred_time_val_marginal  \
0        4.932617   0.019979                 4.513487                4.932617   
1        0.488718  11.308419                 0.759706                0.488718   
2       11.180018  50.632731                 1.232291                0.228870   
3       12.296392  80.121774                 0.007996                0.001000   
4       11.445863  57.646737                 0.610339                0.494716   
5       11.441885  45.150910                 0.654158                0.490737   
6       11.081070  48.836789                 0.424755                0.129922   
7        0.240861  13.674118                 1.763984                0.240861   
8        5.580241  29.820806                 0.006873                0.001000   
9        0.405763  15.789905                 2.594505                0.405763   
10      11.053090  62.200799                 0.169509                0.101942   
11       4.883188   0.022986                 5.105061                4.883188   

    fit_time_marginal  stack_level  can_infer  fit_order  
0            0.019979            1       True          2  
1           11.308419            1       True          5  
2            9.817325            2       True          7  
3            0.300826            3       True         12  
4           16.831331            2       True          9  
5            4.335504            2       True         11  
6            8.021383            2       True          8  
7           13.674118            1       True          4  
8            0.336804            2       True          6  
9           15.789905            1       True          3  
10          21.385392            2       True         10  
11           0.022986            1       True          1  
                     model    score_test  score_val  pred_time_test  \
0    KNeighborsDist_BAG_L1 -2.068240e-07 -47.877308        4.335899   
1     ExtraTreesMSE_BAG_L1 -1.424137e+01 -38.633465        0.641069   
2   RandomForestMSE_BAG_L1 -1.476350e+01 -39.950767        0.649731   
3        LightGBMXT_BAG_L2 -1.598340e+01 -32.417824       15.564713   
4      WeightedEnsemble_L3 -1.681246e+01 -31.703901       16.649247   
5   RandomForestMSE_BAG_L2 -1.701187e+01 -32.695044       14.977512   
6     ExtraTreesMSE_BAG_L2 -1.756622e+01 -31.979331       14.964303   
7          LightGBM_BAG_L1 -1.760263e+01 -33.671413        1.693025   
8          CatBoost_BAG_L2 -1.777008e+01 -32.895921       14.490366   
9          LightGBM_BAG_L2 -1.800741e+01 -32.158074       14.745185   
10     WeightedEnsemble_L2 -1.901976e+01 -31.591619        8.802482   
11       LightGBMXT_BAG_L1 -1.939929e+01 -31.748761        2.495561   
12         CatBoost_BAG_L1 -3.100732e+01 -36.213989        0.270181   
13   KNeighborsUnif_BAG_L1 -3.893895e+01 -48.481624        4.230965   

    pred_time_val    fit_time  pred_time_test_marginal  \
0        4.760259    0.025984                 4.335899   
1        0.429775    3.177173                 0.641069   
2        0.397770   11.095634                 0.649731   
3       10.937941  158.161819                 1.248281   
4       11.558583  169.613444                 0.007910   
5       11.295735  164.052448                 0.661080   
6       11.205789  152.325426                 0.647871   
7        0.235863   13.246117                 1.693025   
8       10.806980  186.469009                 0.173934   
9       10.841994  155.528358                 0.428753   
10       5.479951  134.451462                 0.007815   
11       0.387892   17.197382                 2.495561   
12       0.094939  103.588206                 0.270181   
13       4.407574    0.017992                 4.230965   

    pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  \
0                 4.760259           0.025984            1       True   
1                 0.429775           3.177173            1       True   
2                 0.397770          11.095634            1       True   
3                 0.223871           9.813329            2       True   
4                 0.000999           0.294821            3       True   
5                 0.581664          15.703958            2       True   
6                 0.491719           3.976936            2       True   
7                 0.235863          13.246117            1       True   
8                 0.092910          38.120519            2       True   
9                 0.127924           7.179868            2       True   
10                0.000999           0.393772            2       True   
11                0.387892          17.197382            1       True   
12                0.094939         103.588206            1       True   
13                4.407574           0.017992            1       True   

    fit_order  
0           2  
1           7  
2           5  
3           9  
4          14  
5          11  
6          13  
7           4  
8          12  
9          10  
10          8  
11          3  
12          6  
13          1

results[2]

# Remember to set all negative values to zero
predictions_time_search_5min = predictor_time_search.predict(df_test)
predictions_time_search_5min[predictions_time_search_5min<0] = 0

# Same submitting predictions
submission_new_features = pd.read_csv('sampleSubmission.csv')
submission_new_features["count"] = predictions_time_search_5min
submission_new_features.to_csv("submission_time_search_5min.csv", index=False)

!kaggle competitions submit -c bike-sharing-demand -f submission_time_search_5min.csv -m "5 min train submit"
!kaggle competitions submissions -c bike-sharing-demand

Successfully submitted to Bike Sharing Demand

c:\users\hp\anaconda3\envs\aws  deep learning\lib\site-packages\cryptography\hazmat\backends\openssl\x509.py:17: CryptographyDeprecationWarning: This version of cryptography contains a temporary pyOpenSSL fallback path. Upgrade pyOpenSSL now.
  utils.DeprecatedIn35,

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fileName                                 date                 description                                   status    publicScore  privateScore  
---------------------------------------  -------------------  --------------------------------------------  --------  -----------  ------------  
submission_time_search_5min.csv          2021-12-11 10:12:57  5 min train submit                            complete  0.44793      0.44793       
submission_submission_hp_v4.csv          2021-12-11 09:09:50  new hyperparameters V4                        complete  0.48757      0.48757       
submission_submission_hp_v4.csv          2021-12-11 08:52:06  new hyperparameters V4                        complete  0.48080      0.48080       
submission.csv                           2021-12-11 08:34:12  first raw submission                          complete  1.40041      1.40041       
submission_submission_hp_v4.csv          2021-12-10 18:11:50  new hyperparameters V4                        complete  0.45257      0.45257       
submission_submission_hp_v4.csv          2021-12-10 16:42:13  new hyperparameters V4                        complete  0.46035      0.46035       
submission_submission_RMSLE.csv          2021-12-10 14:44:00  Custome Eval Metric                           complete  0.45892      0.45892       
submission_submission_hypov2.csv         2021-12-10 14:23:31  new features with hyperparameters V2          complete  0.45490      0.45490       
submission_submission_hypov2.csv         2021-12-10 13:57:06  new features with hyperparameters V2          complete  0.57647      0.57647       
submission_submission_hypo.csv           2021-12-10 10:07:22  new features with hyperparameters             complete  0.45563      0.45563       
submission_time_search_5min.csv          2021-12-10 09:23:07  5 min train submit                            complete  0.44797      0.44797       
submission_import_features.csv           2021-12-08 13:55:39  Feature Importance                            complete  0.45194      0.45194       
submission_import_features.csv           2021-12-08 13:53:11  Feature Importance                            complete  0.45414      0.45414       
submission_import_features.csv           2021-12-08 13:51:11  Feature Importance                            complete  0.45749      0.45749       
submission_import_features.csv           2021-12-08 13:48:53  Feature Importance                            complete  0.45210      0.45210       
submission_import_features.csv           2021-12-08 13:44:55  Feature Importance                            complete  0.48275      0.48275       
submission_import_features.csv           2021-12-08 13:42:28  Feature Importance                            complete  0.45753      0.45753       
submission_import_features.csv           2021-12-08 13:39:01  Feature Importance                            complete  0.45939      0.45939       
submission_new_featuresV3.csv            2021-12-08 12:44:41  new features_AdvancedV3                       complete  0.45274      0.45274       
submission_new_features_Advanced_V1.csv  2021-12-08 12:20:43  new features Advanced V1                      complete  0.45810      0.45810       
submission_new_features_Advanced_V1.csv  2021-12-08 12:20:12  new features                                  complete  0.45810      0.45810       
submission_new_features_log.csv          2021-12-08 12:07:08  NF - Model 2  (Basic  Log Transform )         complete  0.50789      0.50789       
submission_new_features_log.csv          2021-12-08 12:06:06  NF - Model 2  (Basic  Log Transform )         complete  0.50674      0.50674       
submission_new_features_log.csv          2021-12-08 11:34:53  NF - Model 2  (Basic  Log Transform )         complete  0.50674      0.50674       
submission_new_features.csv              2021-12-08 11:24:55  NF - Model 1  (Basic  Time Catogricla data )  complete  0.50674      0.50674       
submission.csv                           2021-12-08 11:13:27  first raw submission                          complete  1.38952      1.38952       
submission_new_hpo.csv                   2021-12-08 11:06:33  new features with hyperparameters Tune        complete  0.48748      0.48748       
submission_new_hpo.csv                   2021-12-08 09:14:10  new features with hyperparameters Tune        complete  0.51858      0.51858       
submission_new_hpo_ensV4.csv             2021-12-07 15:02:09  new features with hyperparameters Vקרדןםמ 4   complete  0.48961      0.48961       
submission_new_hpo.csv                   2021-12-07 14:29:38  new features with hyperparameters Tune        complete  0.48338      0.48338       
submission_new_featuresV3.csv            2021-12-07 14:00:06  new featuresV3                                complete  0.45074      0.45074       
submission_new_featuresV2.csv            2021-12-07 13:39:45  new features                                  complete  0.50306      0.50306       
submission_new_features.csv              2021-12-07 13:09:48  new features                                  complete  0.51097      0.51097       
submission.csv                           2021-12-07 13:04:23  first raw submission                          complete  1.39309      1.39309       
submission_new_hpo.csv                   2021-12-07 11:12:13  new features with hyperparameters Tune        complete  0.45509      0.45509       
submission_new_featuresV3.csv            2021-12-07 10:53:43  new featuresV3                                complete  0.45057      0.45057       
submission_new_featuresV2.csv            2021-12-07 10:43:05  new features                                  complete  0.47595      0.47595       
submission_new_featuresV2.csv            2021-12-07 10:42:32  new features                                  error     None         None          
submission_new_features.csv              2021-12-07 10:32:26  new features                                  complete  0.50670      0.50670       
submission.csv                           2021-12-07 10:24:27  first raw submission                          complete  1.38989      1.38989       
submission_new_hpo.csv                   2021-12-07 09:08:34  new features with hyperparameters             complete  0.46172      0.46172       
submission_new_hpo.csv                   2021-12-07 08:59:07  new features with hyperparameters             complete  0.45760      0.45760       
submission_new_features.csv              2021-12-06 20:57:01  new features                                  complete  0.44755      0.44755       
submission_new_features.csv              2021-12-06 20:50:18  new features                                  complete  0.44577      0.44577       
submission_new_features.csv              2021-12-06 20:45:43  new features                                  complete  0.44592      0.44592       
submission_RMSLE.csv                     2021-12-06 17:57:57  Custome Eval Metric                           complete  0.46093      0.46093       
submission_RMSLE.csv                     2021-12-06 17:46:31  Custome Eval Metric                           complete  0.46093      0.46093       
submission_RMSLE.csv                     2021-12-06 17:25:04  Custome Eval Metric                           complete  0.46093      0.46093       
submission_RMSLE.csv                     2021-12-06 17:13:44  Custome Eval Metric                           complete  0.46093      0.46093       
submission_new_hpo_ens.csv               2021-12-06 15:59:53  new features with hyperparameters             complete  0.44685      0.44685

c:\users\hp\anaconda3\envs\aws  deep learning\lib\site-packages\cryptography\hazmat\backends\openssl\x509.py:17: CryptographyDeprecationWarning: This version of cryptography contains a temporary pyOpenSSL fallback path. Upgrade pyOpenSSL now.
  utils.DeprecatedIn35,

New Score of : 0.44793¶

## 6A - More Paramters tuning

import autogluon.core as ag
 
nn_options = {  # specifies non-default hyperparameter values for neural network models
    'num_epochs': 10,  # number of training epochs (controls training time of NN models)
    'learning_rate': ag.space.Real(1e-4, 1e-2, default=5e-4, log=True),  # learning rate used in training (real-valued hyperparameter searched on log-scale)
    'activation': ag.space.Categorical('relu', 'softrelu', 'tanh'),  # activation function used in NN (categorical hyperparameter, default = first entry)
    'layers': ag.space.Categorical([100], [1000], [200, 100], [300, 200, 100]),  # each choice for categorical hyperparameter 'layers' corresponds to list of sizes for each NN layer to use
    'dropout_prob': ag.space.Real(0.0, 0.5, default=0.1),  # dropout probability (real-valued hyperparameter)
}

gbm_options = {  # specifies non-default hyperparameter values for lightGBM gradient boosted trees
    'num_boost_round': 10000,  # number of boosting rounds (controls training time of GBM models)
    #'num_leaves': ag.space.Int(lower=26, upper=66, default=36),  # number of leaves in trees (integer hyperparameter)
}


time_limit = 15*60  # train various models for ~2 min
num_trials = 5  # try at most 5 different hyperparameter configurations for each type of model
search_strategy = 'auto'  # to tune hyperparameters using Bayesian optimization routine with a local scheduler

hyperparameter_tune = {  # HPO is not performed unless hyperparameter_tune_kwargs is specified
    'num_trials': num_trials,
    'scheduler' : 'local',
    'searcher': search_strategy,
}



predictor_new_hpo_v2 = TabularPredictor(label='count', eval_metric='root_mean_squared_error').fit(
    train_data=df_features,   time_limit=time_limit,    
    
     
       
    
    
    hyperparameter_tune_kwargs = hyperparameter_tune,
     presets="best_quality"
     
    
    
    
)

No path specified. Models will be saved in: "AutogluonModels/ag-20211210_140936\"
Presets specified: ['best_quality']
Warning: hyperparameter tuning is currently experimental and may cause the process to hang.
Beginning AutoGluon training ... Time limit = 900s
AutoGluon will save models to "AutogluonModels/ag-20211210_140936\"
AutoGluon Version:  0.3.1
Train Data Rows:    10886
Train Data Columns: 25
Preprocessing data ...
AutoGluon infers your prediction problem is: 'regression' (because dtype of label-column == float and many unique label-values observed).
	Label info (max, min, mean, stddev): (977.0, 1.0, 191.57413, 181.14445)
	If 'regression' is not the correct problem_type, please manually specify the problem_type argument in fit() (You may specify problem_type as one of: ['binary', 'multiclass', 'regression'])
Using Feature Generators to preprocess the data ...
Fitting AutoMLPipelineFeatureGenerator...
	Available Memory:                    1829.94 MB
	Train Data (Original)  Memory Usage: 1.72 MB (0.1% of available memory)
	Inferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.
	Stage 1 Generators:
		Fitting AsTypeFeatureGenerator...
			Note: Converting 1 features to boolean dtype as they only contain 2 unique values.
	Stage 2 Generators:
		Fitting FillNaFeatureGenerator...
	Stage 3 Generators:
		Fitting IdentityFeatureGenerator...
		Fitting CategoryFeatureGenerator...
			Fitting CategoryMemoryMinimizeFeatureGenerator...
		Fitting DatetimeFeatureGenerator...
	Stage 4 Generators:
		Fitting DropUniqueFeatureGenerator...
	Types of features in original data (raw dtype, special dtypes):
		('category', []) :  6 | ['hour', 'workingday', 'weather', 'weekday', 'day', ...]
		('datetime', []) :  2 | ['datetime', 'date']
		('float', [])    : 16 | ['shift_1h', 'shift_7', 'shift_1', 'rollind_24h_std', 'shift_5h', ...]
		('int', [])      :  1 | ['humidity']
	Types of features in processed data (raw dtype, special dtypes):
		('category', [])             :  5 | ['hour', 'weather', 'weekday', 'day', 'month']
		('float', [])                : 16 | ['shift_1h', 'shift_7', 'shift_1', 'rollind_24h_std', 'shift_5h', ...]
		('int', [])                  :  1 | ['humidity']
		('int', ['bool'])            :  1 | ['workingday']
		('int', ['datetime_as_int']) :  2 | ['datetime', 'date']
	0.4s = Fit runtime
	25 features in original data used to generate 25 features in processed data.
	Train Data (Processed) Memory Usage: 1.72 MB (0.1% of available memory)
Data preprocessing and feature engineering runtime = 0.46s ...
AutoGluon will gauge predictive performance using evaluation metric: 'root_mean_squared_error'
	To change this, specify the eval_metric argument of fit()
AutoGluon will fit 2 stack levels (L1 to L2) ...
Fitting 11 L1 models ...
Hyperparameter tuning model: KNeighborsUnif_BAG_L1 ...
Fitted model: KNeighborsUnif_BAG_L1\T0 ...
	-48.4816	 = Validation score   (root_mean_squared_error)
	0.02s	 = Training   runtime
	9.44s	 = Validation runtime
Hyperparameter tuning model: KNeighborsDist_BAG_L1 ...
Fitted model: KNeighborsDist_BAG_L1\T0 ...
	-47.8773	 = Validation score   (root_mean_squared_error)
	0.02s	 = Training   runtime
	10.03s	 = Validation runtime
Hyperparameter tuning model: LightGBMXT_BAG_L1 ...
Warning: Exception caused LightGBMXT_BAG_L1 to fail during hyperparameter tuning... Skipping this model.
Traceback (most recent call last):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\tabular\trainer\abstract_trainer.py", line 1153, in _train_single_full
    hpo_models, hpo_model_performances, hpo_results = model.hyperparameter_tune(X=X, y=y, k_fold=k_fold, scheduler_options=hyperparameter_tune_kwargs, **model_fit_kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 830, in hyperparameter_tune
    return self._hyperparameter_tune(scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\stacker_ensemble_model.py", line 179, in _hyperparameter_tune
    return super()._hyperparameter_tune(X=X, y=y, k_fold=k_fold, scheduler_options=scheduler_options, preprocess_kwargs=preprocess_kwargs, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 801, in _hyperparameter_tune
    hpo_models, hpo_model_performances, hpo_results = self.model_base.hyperparameter_tune(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 830, in hyperparameter_tune
    return self._hyperparameter_tune(scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\tabular\models\lgb\lgb_model.py", line 391, in _hyperparameter_tune
    scheduler.run()
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\scheduler\seq_scheduler.py", line 152, in run
    for i in (tqdm(r) if self.num_trials < 1000 else r):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 242, in __init__
    self.container = self.status_printer(self.fp, total, self.desc, self.ncols)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 116, in status_printer
    "IProgress not found. Please update jupyter and ipywidgets."
ImportError: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
Exception ignored in: <bound method tqdm.__del__ of <tqdm.auto.tqdm object at 0x0000029783522240>>
Traceback (most recent call last):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\std.py", line 1147, in __del__
    self.close()
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 286, in close
    self.disp(bar_style='danger', check_delay=False)
AttributeError: 'tqdm' object has no attribute 'disp'
Hyperparameter tuning model: LightGBM_BAG_L1 ...
Warning: Exception caused LightGBM_BAG_L1 to fail during hyperparameter tuning... Skipping this model.
Traceback (most recent call last):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\tabular\trainer\abstract_trainer.py", line 1153, in _train_single_full
    hpo_models, hpo_model_performances, hpo_results = model.hyperparameter_tune(X=X, y=y, k_fold=k_fold, scheduler_options=hyperparameter_tune_kwargs, **model_fit_kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 830, in hyperparameter_tune
    return self._hyperparameter_tune(scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\stacker_ensemble_model.py", line 179, in _hyperparameter_tune
    return super()._hyperparameter_tune(X=X, y=y, k_fold=k_fold, scheduler_options=scheduler_options, preprocess_kwargs=preprocess_kwargs, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 801, in _hyperparameter_tune
    hpo_models, hpo_model_performances, hpo_results = self.model_base.hyperparameter_tune(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 830, in hyperparameter_tune
    return self._hyperparameter_tune(scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\tabular\models\lgb\lgb_model.py", line 391, in _hyperparameter_tune
    scheduler.run()
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\scheduler\seq_scheduler.py", line 152, in run
    for i in (tqdm(r) if self.num_trials < 1000 else r):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 242, in __init__
    self.container = self.status_printer(self.fp, total, self.desc, self.ncols)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 116, in status_printer
    "IProgress not found. Please update jupyter and ipywidgets."
ImportError: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
Exception ignored in: <bound method tqdm.__del__ of <tqdm.auto.tqdm object at 0x0000029743B08048>>
Traceback (most recent call last):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\std.py", line 1147, in __del__
    self.close()
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 286, in close
    self.disp(bar_style='danger', check_delay=False)
AttributeError: 'tqdm' object has no attribute 'disp'
Hyperparameter tuning model: RandomForestMSE_BAG_L1 ...
Fitted model: RandomForestMSE_BAG_L1\T0 ...
	-39.9508	 = Validation score   (root_mean_squared_error)
	19.22s	 = Training   runtime
	0.92s	 = Validation runtime
Hyperparameter tuning model: CatBoost_BAG_L1 ...
Warning: Exception caused CatBoost_BAG_L1 to fail during hyperparameter tuning... Skipping this model.
Traceback (most recent call last):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\tabular\trainer\abstract_trainer.py", line 1153, in _train_single_full
    hpo_models, hpo_model_performances, hpo_results = model.hyperparameter_tune(X=X, y=y, k_fold=k_fold, scheduler_options=hyperparameter_tune_kwargs, **model_fit_kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 830, in hyperparameter_tune
    return self._hyperparameter_tune(scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\stacker_ensemble_model.py", line 179, in _hyperparameter_tune
    return super()._hyperparameter_tune(X=X, y=y, k_fold=k_fold, scheduler_options=scheduler_options, preprocess_kwargs=preprocess_kwargs, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 801, in _hyperparameter_tune
    hpo_models, hpo_model_performances, hpo_results = self.model_base.hyperparameter_tune(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 830, in hyperparameter_tune
    return self._hyperparameter_tune(scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 887, in _hyperparameter_tune
    scheduler.run()
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\scheduler\seq_scheduler.py", line 152, in run
    for i in (tqdm(r) if self.num_trials < 1000 else r):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 242, in __init__
    self.container = self.status_printer(self.fp, total, self.desc, self.ncols)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 116, in status_printer
    "IProgress not found. Please update jupyter and ipywidgets."
ImportError: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
Exception ignored in: <bound method tqdm.__del__ of <tqdm.auto.tqdm object at 0x00000297446C3B70>>
Traceback (most recent call last):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\std.py", line 1147, in __del__
    self.close()
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 286, in close
    self.disp(bar_style='danger', check_delay=False)
AttributeError: 'tqdm' object has no attribute 'disp'
Hyperparameter tuning model: ExtraTreesMSE_BAG_L1 ...
Fitted model: ExtraTreesMSE_BAG_L1\T0 ...
	-38.6335	 = Validation score   (root_mean_squared_error)
	5.98s	 = Training   runtime
	1.0s	 = Validation runtime
Hyperparameter tuning model: NeuralNetFastAI_BAG_L1 ...
	Ran out of time, stopping training early. (Stopping on epoch 3)
Fitted model: NeuralNetFastAI_BAG_L1\T0 ...
	-47.6783	 = Validation score   (root_mean_squared_error)
	3.65s	 = Training   runtime
	0.06s	 = Validation runtime
Hyperparameter tuning model: XGBoost_BAG_L1 ...
Warning: Exception caused XGBoost_BAG_L1 to fail during hyperparameter tuning... Skipping this model.
Traceback (most recent call last):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\tabular\trainer\abstract_trainer.py", line 1153, in _train_single_full
    hpo_models, hpo_model_performances, hpo_results = model.hyperparameter_tune(X=X, y=y, k_fold=k_fold, scheduler_options=hyperparameter_tune_kwargs, **model_fit_kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 830, in hyperparameter_tune
    return self._hyperparameter_tune(scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\stacker_ensemble_model.py", line 179, in _hyperparameter_tune
    return super()._hyperparameter_tune(X=X, y=y, k_fold=k_fold, scheduler_options=scheduler_options, preprocess_kwargs=preprocess_kwargs, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 801, in _hyperparameter_tune
    hpo_models, hpo_model_performances, hpo_results = self.model_base.hyperparameter_tune(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 830, in hyperparameter_tune
    return self._hyperparameter_tune(scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 887, in _hyperparameter_tune
    scheduler.run()
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\scheduler\seq_scheduler.py", line 152, in run
    for i in (tqdm(r) if self.num_trials < 1000 else r):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 242, in __init__
    self.container = self.status_printer(self.fp, total, self.desc, self.ncols)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 116, in status_printer
    "IProgress not found. Please update jupyter and ipywidgets."
ImportError: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
Exception ignored in: <bound method tqdm.__del__ of <tqdm.auto.tqdm object at 0x000002977FE98550>>
Traceback (most recent call last):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\std.py", line 1147, in __del__
    self.close()
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 286, in close
    self.disp(bar_style='danger', check_delay=False)
AttributeError: 'tqdm' object has no attribute 'disp'
Hyperparameter tuning model: NeuralNetMXNet_BAG_L1 ...
Warning: Exception caused NeuralNetMXNet_BAG_L1 to fail during hyperparameter tuning... Skipping this model.
Traceback (most recent call last):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\tabular\trainer\abstract_trainer.py", line 1153, in _train_single_full
    hpo_models, hpo_model_performances, hpo_results = model.hyperparameter_tune(X=X, y=y, k_fold=k_fold, scheduler_options=hyperparameter_tune_kwargs, **model_fit_kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 830, in hyperparameter_tune
    return self._hyperparameter_tune(scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\stacker_ensemble_model.py", line 179, in _hyperparameter_tune
    return super()._hyperparameter_tune(X=X, y=y, k_fold=k_fold, scheduler_options=scheduler_options, preprocess_kwargs=preprocess_kwargs, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 801, in _hyperparameter_tune
    hpo_models, hpo_model_performances, hpo_results = self.model_base.hyperparameter_tune(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 830, in hyperparameter_tune
    return self._hyperparameter_tune(scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\tabular\models\tabular_nn\tabular_nn_model.py", line 749, in _hyperparameter_tune
    scheduler.run()
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\scheduler\seq_scheduler.py", line 152, in run
    for i in (tqdm(r) if self.num_trials < 1000 else r):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 242, in __init__
    self.container = self.status_printer(self.fp, total, self.desc, self.ncols)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 116, in status_printer
    "IProgress not found. Please update jupyter and ipywidgets."
ImportError: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
Exception ignored in: <bound method tqdm.__del__ of <tqdm.auto.tqdm object at 0x000002977FF21A20>>
Traceback (most recent call last):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\std.py", line 1147, in __del__
    self.close()
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 286, in close
    self.disp(bar_style='danger', check_delay=False)
AttributeError: 'tqdm' object has no attribute 'disp'
Fitting model: LightGBMLarge_BAG_L1 ... Training model for up to 4.91s of the 817.7s of remaining time.
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	Ran out of time, early stopping on iteration 164. Best iteration is:
	[164]	train_set's rmse: 16.0395	valid_set's rmse: 35.0758
	-35.0758	 = Validation score   (root_mean_squared_error)
	4.1s	 = Training   runtime
	0.04s	 = Validation runtime
Fitting model: KNeighborsUnif_BAG_L1\T0 ... Training model for up to 513.4s of the 813.39s of remaining time.
	-48.4816	 = Validation score   (root_mean_squared_error)
	0.02s	 = Training   runtime
	9.44s	 = Validation runtime
Fitting model: KNeighborsDist_BAG_L1\T0 ... Training model for up to 513.37s of the 813.36s of remaining time.
	-47.8773	 = Validation score   (root_mean_squared_error)
	0.02s	 = Training   runtime
	10.03s	 = Validation runtime
Fitting model: RandomForestMSE_BAG_L1\T0 ... Training model for up to 513.35s of the 813.34s of remaining time.
	-39.9508	 = Validation score   (root_mean_squared_error)
	19.22s	 = Training   runtime
	0.92s	 = Validation runtime
Fitting model: ExtraTreesMSE_BAG_L1\T0 ... Training model for up to 513.32s of the 813.3s of remaining time.
	-38.6335	 = Validation score   (root_mean_squared_error)
	5.98s	 = Training   runtime
	1.0s	 = Validation runtime
Fitting model: NeuralNetFastAI_BAG_L1\T0 ... Training model for up to 513.29s of the 813.27s of remaining time.
	-32.8541	 = Validation score   (root_mean_squared_error)
	243.52s	 = Training   runtime
	0.79s	 = Validation runtime
Fitting model: LightGBMLarge_BAG_L1 ... Training model for up to 272.3s of the 572.28s of remaining time.
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "

[1000]	train_set's rmse: 2.32004	valid_set's rmse: 32.4143

	Ran out of time, early stopping on iteration 1480. Best iteration is:
	[1282]	train_set's rmse: 1.35623	valid_set's rmse: 32.3944
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "

[1000]	train_set's rmse: 2.31283	valid_set's rmse: 34.4461

C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "

[1000]	train_set's rmse: 2.39631	valid_set's rmse: 35.0219

C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	-35.2255	 = Validation score   (root_mean_squared_error)
	134.37s	 = Training   runtime
	0.79s	 = Validation runtime
Completed 1/20 k-fold bagging repeats ...
Fitting model: WeightedEnsemble_L2 ... Training model for up to 360.0s of the 438.05s of remaining time.
	-31.629	 = Validation score   (root_mean_squared_error)
	0.39s	 = Training   runtime
	0.0s	 = Validation runtime
Fitting 9 L2 models ...
Hyperparameter tuning model: LightGBMXT_BAG_L2 ...
Warning: Exception caused LightGBMXT_BAG_L2 to fail during hyperparameter tuning... Skipping this model.
Traceback (most recent call last):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\tabular\trainer\abstract_trainer.py", line 1153, in _train_single_full
    hpo_models, hpo_model_performances, hpo_results = model.hyperparameter_tune(X=X, y=y, k_fold=k_fold, scheduler_options=hyperparameter_tune_kwargs, **model_fit_kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 830, in hyperparameter_tune
    return self._hyperparameter_tune(scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\stacker_ensemble_model.py", line 179, in _hyperparameter_tune
    return super()._hyperparameter_tune(X=X, y=y, k_fold=k_fold, scheduler_options=scheduler_options, preprocess_kwargs=preprocess_kwargs, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 801, in _hyperparameter_tune
    hpo_models, hpo_model_performances, hpo_results = self.model_base.hyperparameter_tune(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 830, in hyperparameter_tune
    return self._hyperparameter_tune(scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\tabular\models\lgb\lgb_model.py", line 391, in _hyperparameter_tune
    scheduler.run()
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\scheduler\seq_scheduler.py", line 152, in run
    for i in (tqdm(r) if self.num_trials < 1000 else r):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 242, in __init__
    self.container = self.status_printer(self.fp, total, self.desc, self.ncols)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 116, in status_printer
    "IProgress not found. Please update jupyter and ipywidgets."
ImportError: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
Exception ignored in: <bound method tqdm.__del__ of <tqdm.auto.tqdm object at 0x000002977FF214A8>>
Traceback (most recent call last):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\std.py", line 1147, in __del__
    self.close()
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 286, in close
    self.disp(bar_style='danger', check_delay=False)
AttributeError: 'tqdm' object has no attribute 'disp'
Hyperparameter tuning model: LightGBM_BAG_L2 ...
Warning: Exception caused LightGBM_BAG_L2 to fail during hyperparameter tuning... Skipping this model.
Traceback (most recent call last):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\tabular\trainer\abstract_trainer.py", line 1153, in _train_single_full
    hpo_models, hpo_model_performances, hpo_results = model.hyperparameter_tune(X=X, y=y, k_fold=k_fold, scheduler_options=hyperparameter_tune_kwargs, **model_fit_kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 830, in hyperparameter_tune
    return self._hyperparameter_tune(scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\stacker_ensemble_model.py", line 179, in _hyperparameter_tune
    return super()._hyperparameter_tune(X=X, y=y, k_fold=k_fold, scheduler_options=scheduler_options, preprocess_kwargs=preprocess_kwargs, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 801, in _hyperparameter_tune
    hpo_models, hpo_model_performances, hpo_results = self.model_base.hyperparameter_tune(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 830, in hyperparameter_tune
    return self._hyperparameter_tune(scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\tabular\models\lgb\lgb_model.py", line 391, in _hyperparameter_tune
    scheduler.run()
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\scheduler\seq_scheduler.py", line 152, in run
    for i in (tqdm(r) if self.num_trials < 1000 else r):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 242, in __init__
    self.container = self.status_printer(self.fp, total, self.desc, self.ncols)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 116, in status_printer
    "IProgress not found. Please update jupyter and ipywidgets."
ImportError: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
Exception ignored in: <bound method tqdm.__del__ of <tqdm.auto.tqdm object at 0x000002977FF21710>>
Traceback (most recent call last):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\std.py", line 1147, in __del__
    self.close()
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 286, in close
    self.disp(bar_style='danger', check_delay=False)
AttributeError: 'tqdm' object has no attribute 'disp'
Hyperparameter tuning model: RandomForestMSE_BAG_L2 ...
Fitted model: RandomForestMSE_BAG_L2\T0 ...
	-32.5832	 = Validation score   (root_mean_squared_error)
	17.54s	 = Training   runtime
	0.95s	 = Validation runtime
Hyperparameter tuning model: CatBoost_BAG_L2 ...
Warning: Exception caused CatBoost_BAG_L2 to fail during hyperparameter tuning... Skipping this model.
Traceback (most recent call last):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\tabular\trainer\abstract_trainer.py", line 1153, in _train_single_full
    hpo_models, hpo_model_performances, hpo_results = model.hyperparameter_tune(X=X, y=y, k_fold=k_fold, scheduler_options=hyperparameter_tune_kwargs, **model_fit_kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 830, in hyperparameter_tune
    return self._hyperparameter_tune(scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\stacker_ensemble_model.py", line 179, in _hyperparameter_tune
    return super()._hyperparameter_tune(X=X, y=y, k_fold=k_fold, scheduler_options=scheduler_options, preprocess_kwargs=preprocess_kwargs, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 801, in _hyperparameter_tune
    hpo_models, hpo_model_performances, hpo_results = self.model_base.hyperparameter_tune(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 830, in hyperparameter_tune
    return self._hyperparameter_tune(scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 887, in _hyperparameter_tune
    scheduler.run()
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\scheduler\seq_scheduler.py", line 152, in run
    for i in (tqdm(r) if self.num_trials < 1000 else r):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 242, in __init__
    self.container = self.status_printer(self.fp, total, self.desc, self.ncols)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 116, in status_printer
    "IProgress not found. Please update jupyter and ipywidgets."
ImportError: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
Exception ignored in: <bound method tqdm.__del__ of <tqdm.auto.tqdm object at 0x000002977FEA0518>>
Traceback (most recent call last):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\std.py", line 1147, in __del__
    self.close()
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 286, in close
    self.disp(bar_style='danger', check_delay=False)
AttributeError: 'tqdm' object has no attribute 'disp'
Hyperparameter tuning model: ExtraTreesMSE_BAG_L2 ...
Fitted model: ExtraTreesMSE_BAG_L2\T0 ...
	-32.0732	 = Validation score   (root_mean_squared_error)
	5.69s	 = Training   runtime
	0.95s	 = Validation runtime
Hyperparameter tuning model: NeuralNetFastAI_BAG_L2 ...
	Ran out of time, stopping training early. (Stopping on epoch 4)
Fitted model: NeuralNetFastAI_BAG_L2\T0 ...
	-36.1323	 = Validation score   (root_mean_squared_error)
	3.45s	 = Training   runtime
	0.05s	 = Validation runtime
Hyperparameter tuning model: XGBoost_BAG_L2 ...
Warning: Exception caused XGBoost_BAG_L2 to fail during hyperparameter tuning... Skipping this model.
Traceback (most recent call last):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\tabular\trainer\abstract_trainer.py", line 1153, in _train_single_full
    hpo_models, hpo_model_performances, hpo_results = model.hyperparameter_tune(X=X, y=y, k_fold=k_fold, scheduler_options=hyperparameter_tune_kwargs, **model_fit_kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 830, in hyperparameter_tune
    return self._hyperparameter_tune(scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\stacker_ensemble_model.py", line 179, in _hyperparameter_tune
    return super()._hyperparameter_tune(X=X, y=y, k_fold=k_fold, scheduler_options=scheduler_options, preprocess_kwargs=preprocess_kwargs, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 801, in _hyperparameter_tune
    hpo_models, hpo_model_performances, hpo_results = self.model_base.hyperparameter_tune(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 830, in hyperparameter_tune
    return self._hyperparameter_tune(scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 887, in _hyperparameter_tune
    scheduler.run()
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\scheduler\seq_scheduler.py", line 152, in run
    for i in (tqdm(r) if self.num_trials < 1000 else r):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 242, in __init__
    self.container = self.status_printer(self.fp, total, self.desc, self.ncols)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 116, in status_printer
    "IProgress not found. Please update jupyter and ipywidgets."
ImportError: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
Exception ignored in: <bound method tqdm.__del__ of <tqdm.auto.tqdm object at 0x0000029783467C50>>
Traceback (most recent call last):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\std.py", line 1147, in __del__
    self.close()
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 286, in close
    self.disp(bar_style='danger', check_delay=False)
AttributeError: 'tqdm' object has no attribute 'disp'
Hyperparameter tuning model: NeuralNetMXNet_BAG_L2 ...
Warning: Exception caused NeuralNetMXNet_BAG_L2 to fail during hyperparameter tuning... Skipping this model.
Traceback (most recent call last):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\tabular\trainer\abstract_trainer.py", line 1153, in _train_single_full
    hpo_models, hpo_model_performances, hpo_results = model.hyperparameter_tune(X=X, y=y, k_fold=k_fold, scheduler_options=hyperparameter_tune_kwargs, **model_fit_kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 830, in hyperparameter_tune
    return self._hyperparameter_tune(scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\stacker_ensemble_model.py", line 179, in _hyperparameter_tune
    return super()._hyperparameter_tune(X=X, y=y, k_fold=k_fold, scheduler_options=scheduler_options, preprocess_kwargs=preprocess_kwargs, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 801, in _hyperparameter_tune
    hpo_models, hpo_model_performances, hpo_results = self.model_base.hyperparameter_tune(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 830, in hyperparameter_tune
    return self._hyperparameter_tune(scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\tabular\models\tabular_nn\tabular_nn_model.py", line 749, in _hyperparameter_tune
    scheduler.run()
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\scheduler\seq_scheduler.py", line 152, in run
    for i in (tqdm(r) if self.num_trials < 1000 else r):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 242, in __init__
    self.container = self.status_printer(self.fp, total, self.desc, self.ncols)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 116, in status_printer
    "IProgress not found. Please update jupyter and ipywidgets."
ImportError: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
Exception ignored in: <bound method tqdm.__del__ of <tqdm.auto.tqdm object at 0x0000029746A417B8>>
Traceback (most recent call last):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\std.py", line 1147, in __del__
    self.close()
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 286, in close
    self.disp(bar_style='danger', check_delay=False)
AttributeError: 'tqdm' object has no attribute 'disp'
Fitting model: LightGBMLarge_BAG_L2 ... Training model for up to 4.38s of the 398.79s of remaining time.
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	-33.9671	 = Validation score   (root_mean_squared_error)
	3.51s	 = Training   runtime
	0.02s	 = Validation runtime
Fitting model: RandomForestMSE_BAG_L2\T0 ... Training model for up to 395.17s of the 395.16s of remaining time.
	-32.5832	 = Validation score   (root_mean_squared_error)
	17.54s	 = Training   runtime
	0.95s	 = Validation runtime
Fitting model: ExtraTreesMSE_BAG_L2\T0 ... Training model for up to 395.14s of the 395.13s of remaining time.
	-32.0732	 = Validation score   (root_mean_squared_error)
	5.69s	 = Training   runtime
	0.95s	 = Validation runtime
Fitting model: NeuralNetFastAI_BAG_L2\T0 ... Training model for up to 395.11s of the 395.1s of remaining time.
	-32.452	 = Validation score   (root_mean_squared_error)
	147.41s	 = Training   runtime
	0.5s	 = Validation runtime
Fitting model: LightGBMLarge_BAG_L2 ... Training model for up to 250.43s of the 250.42s of remaining time.
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	-33.0195	 = Validation score   (root_mean_squared_error)
	28.02s	 = Training   runtime
	0.2s	 = Validation runtime
Completed 1/20 k-fold bagging repeats ...
Fitting model: WeightedEnsemble_L3 ... Training model for up to 360.0s of the 225.21s of remaining time.
	-31.5865	 = Validation score   (root_mean_squared_error)
	0.33s	 = Training   runtime
	0.0s	 = Validation runtime
AutoGluon training complete, total runtime = 675.16s ...
TabularPredictor saved. To load, use: predictor = TabularPredictor.load("AutogluonModels/ag-20211210_140936\")

predictor_new_hpo_v2.leaderboard(df_features, silent=True)

# Remember to set all negative values to zero
predictor_hpo_v2 = predictor_new_hpo_v2.predict(df_test)
predictor_hpo_v2[predictor_hpo_v2<0] = 0

# Same submitting predictions
submission_hypo = pd.read_csv('sampleSubmission.csv')
submission_hypo["count"] = predictor_hpo_v2
submission_hypo.to_csv("submission_submission_hypov2.csv", index=False)

!kaggle competitions submit -c bike-sharing-demand -f submission_submission_hypov2.csv -m "new features with hyperparameters V2  "
!kaggle competitions submissions -c bike-sharing-demand

Successfully submitted to Bike Sharing Demand

c:\users\hp\anaconda3\envs\aws  deep learning\lib\site-packages\cryptography\hazmat\backends\openssl\x509.py:17: CryptographyDeprecationWarning: This version of cryptography contains a temporary pyOpenSSL fallback path. Upgrade pyOpenSSL now.
  utils.DeprecatedIn35,

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  4%|4         | 8.00k/194k [00:00<00:03, 53.3kB/s]
 49%|####9     | 96.0k/194k [00:00<00:00, 447kB/s] 
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100%|##########| 194k/194k [00:02<00:00, 99.3kB/s]

fileName                                 date                 description                                   status    publicScore  privateScore  
---------------------------------------  -------------------  --------------------------------------------  --------  -----------  ------------  
submission_submission_hypov2.csv         2021-12-10 14:23:31  new features with hyperparameters V2          complete  0.45490      0.45490       
submission_submission_hypov2.csv         2021-12-10 13:57:06  new features with hyperparameters V2          complete  0.57647      0.57647       
submission_submission_hypo.csv           2021-12-10 10:07:22  new features with hyperparameters             complete  0.45563      0.45563       
submission_time_search_5min.csv          2021-12-10 09:23:07  5 min train submit                            complete  0.44797      0.44797       
submission_import_features.csv           2021-12-08 13:55:39  Feature Importance                            complete  0.45194      0.45194       
submission_import_features.csv           2021-12-08 13:53:11  Feature Importance                            complete  0.45414      0.45414       
submission_import_features.csv           2021-12-08 13:51:11  Feature Importance                            complete  0.45749      0.45749       
submission_import_features.csv           2021-12-08 13:48:53  Feature Importance                            complete  0.45210      0.45210       
submission_import_features.csv           2021-12-08 13:44:55  Feature Importance                            complete  0.48275      0.48275       
submission_import_features.csv           2021-12-08 13:42:28  Feature Importance                            complete  0.45753      0.45753       
submission_import_features.csv           2021-12-08 13:39:01  Feature Importance                            complete  0.45939      0.45939       
submission_new_featuresV3.csv            2021-12-08 12:44:41  new features_AdvancedV3                       complete  0.45274      0.45274       
submission_new_features_Advanced_V1.csv  2021-12-08 12:20:43  new features Advanced V1                      complete  0.45810      0.45810       
submission_new_features_Advanced_V1.csv  2021-12-08 12:20:12  new features                                  complete  0.45810      0.45810       
submission_new_features_log.csv          2021-12-08 12:07:08  NF - Model 2  (Basic  Log Transform )         complete  0.50789      0.50789       
submission_new_features_log.csv          2021-12-08 12:06:06  NF - Model 2  (Basic  Log Transform )         complete  0.50674      0.50674       
submission_new_features_log.csv          2021-12-08 11:34:53  NF - Model 2  (Basic  Log Transform )         complete  0.50674      0.50674       
submission_new_features.csv              2021-12-08 11:24:55  NF - Model 1  (Basic  Time Catogricla data )  complete  0.50674      0.50674       
submission.csv                           2021-12-08 11:13:27  first raw submission                          complete  1.38952      1.38952       
submission_new_hpo.csv                   2021-12-08 11:06:33  new features with hyperparameters Tune        complete  0.48748      0.48748       
submission_new_hpo.csv                   2021-12-08 09:14:10  new features with hyperparameters Tune        complete  0.51858      0.51858       
submission_new_hpo_ensV4.csv             2021-12-07 15:02:09  new features with hyperparameters Vקרדןםמ 4   complete  0.48961      0.48961       
submission_new_hpo.csv                   2021-12-07 14:29:38  new features with hyperparameters Tune        complete  0.48338      0.48338       
submission_new_featuresV3.csv            2021-12-07 14:00:06  new featuresV3                                complete  0.45074      0.45074       
submission_new_featuresV2.csv            2021-12-07 13:39:45  new features                                  complete  0.50306      0.50306       
submission_new_features.csv              2021-12-07 13:09:48  new features                                  complete  0.51097      0.51097       
submission.csv                           2021-12-07 13:04:23  first raw submission                          complete  1.39309      1.39309       
submission_new_hpo.csv                   2021-12-07 11:12:13  new features with hyperparameters Tune        complete  0.45509      0.45509       
submission_new_featuresV3.csv            2021-12-07 10:53:43  new featuresV3                                complete  0.45057      0.45057       
submission_new_featuresV2.csv            2021-12-07 10:43:05  new features                                  complete  0.47595      0.47595       
submission_new_featuresV2.csv            2021-12-07 10:42:32  new features                                  error     None         None          
submission_new_features.csv              2021-12-07 10:32:26  new features                                  complete  0.50670      0.50670       
submission.csv                           2021-12-07 10:24:27  first raw submission                          complete  1.38989      1.38989       
submission_new_hpo.csv                   2021-12-07 09:08:34  new features with hyperparameters             complete  0.46172      0.46172       
submission_new_hpo.csv                   2021-12-07 08:59:07  new features with hyperparameters             complete  0.45760      0.45760       
submission_new_features.csv              2021-12-06 20:57:01  new features                                  complete  0.44755      0.44755       
submission_new_features.csv              2021-12-06 20:50:18  new features                                  complete  0.44577      0.44577       
submission_new_features.csv              2021-12-06 20:45:43  new features                                  complete  0.44592      0.44592       
submission_RMSLE.csv                     2021-12-06 17:57:57  Custome Eval Metric                           complete  0.46093      0.46093       
submission_RMSLE.csv                     2021-12-06 17:46:31  Custome Eval Metric                           complete  0.46093      0.46093       
submission_RMSLE.csv                     2021-12-06 17:25:04  Custome Eval Metric                           complete  0.46093      0.46093       
submission_RMSLE.csv                     2021-12-06 17:13:44  Custome Eval Metric                           complete  0.46093      0.46093       
submission_new_hpo_ens.csv               2021-12-06 15:59:53  new features with hyperparameters             complete  0.44685      0.44685       
submission_new_hpo_ens.csv               2021-12-06 15:55:19  new features with hyperparameters             complete  0.47243      0.47243       
submission_new_hpo_ens.csv               2021-12-06 15:14:56  new features with hyperparameters             complete  0.44685      0.44685       
submission_new_hpo_ens.csv               2021-12-06 15:11:35  new features with hyperparameters             complete  0.47219      0.47219       
submission_new_hpo_ens.csv               2021-12-06 15:03:25  new features with hyperparameters             complete  0.44685      0.44685       
submission_new_hpo.csv                   2021-12-06 15:02:29  new features with hyperparameters             complete  0.44685      0.44685       
submission_new_hpo.csv                   2021-12-06 13:32:40  new features with hyperparameters             complete  0.44575      0.44575       
submission_new_hpo.csv                   2021-12-06 12:22:42  new features with hyperparameters             complete  0.45943      0.45943

c:\users\hp\anaconda3\envs\aws  deep learning\lib\site-packages\cryptography\hazmat\backends\openssl\x509.py:17: CryptographyDeprecationWarning: This version of cryptography contains a temporary pyOpenSSL fallback path. Upgrade pyOpenSSL now.
  utils.DeprecatedIn35,

import sklearn.metrics
from autogluon.core.metrics import make_scorer
ag_mean_squared_log_error_scorer = make_scorer(name='mean_squared_log_error',
                                           score_func=sklearn.metrics.mean_squared_log_error,
                                           optimum=0,
                                           greater_is_better=False)

 
time_limit = 20*60  # train various models for ~6 min
num_trials = 150  # try at most 5 different hyperparameter configurations for each type of model
search_strategy = 'auto'  # to tune hyperparameters using Bayesian optimization routine with a local scheduler


hyperparameter_tune_kwargs = {  # HPO is not performed unless hyperparameter_tune_kwargs is specified
    'num_trials': num_trials,
    'scheduler' : 'local',
    'searcher': search_strategy,
}

predictor_RMSLE = TabularPredictor(label="count",eval_metric=ag_mean_squared_log_error_scorer).fit(
    train_data=df_features,
    time_limit=time_limit,
    presets="best_quality",
     
    hyperparameter_tune_kwargs=hyperparameter_tune_kwargs,
    #num_bag_folds=3, num_bag_sets=2, num_stack_levels=1 
)

No path specified. Models will be saved in: "AutogluonModels/ag-20211210_143954\"
Presets specified: ['best_quality']
Warning: hyperparameter tuning is currently experimental and may cause the process to hang.
Beginning AutoGluon training ... Time limit = 1200s
AutoGluon will save models to "AutogluonModels/ag-20211210_143954\"
AutoGluon Version:  0.3.1
Train Data Rows:    10886
Train Data Columns: 25
Preprocessing data ...
AutoGluon infers your prediction problem is: 'regression' (because dtype of label-column == float and many unique label-values observed).
	Label info (max, min, mean, stddev): (977.0, 1.0, 191.57413, 181.14445)
	If 'regression' is not the correct problem_type, please manually specify the problem_type argument in fit() (You may specify problem_type as one of: ['binary', 'multiclass', 'regression'])
Using Feature Generators to preprocess the data ...
Fitting AutoMLPipelineFeatureGenerator...
	Available Memory:                    2883.83 MB
	Train Data (Original)  Memory Usage: 1.72 MB (0.1% of available memory)
	Inferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.
	Stage 1 Generators:
		Fitting AsTypeFeatureGenerator...
			Note: Converting 1 features to boolean dtype as they only contain 2 unique values.
	Stage 2 Generators:
		Fitting FillNaFeatureGenerator...
	Stage 3 Generators:
		Fitting IdentityFeatureGenerator...
		Fitting CategoryFeatureGenerator...
			Fitting CategoryMemoryMinimizeFeatureGenerator...
		Fitting DatetimeFeatureGenerator...
	Stage 4 Generators:
		Fitting DropUniqueFeatureGenerator...
	Types of features in original data (raw dtype, special dtypes):
		('category', []) :  6 | ['hour', 'workingday', 'weather', 'weekday', 'day', ...]
		('datetime', []) :  2 | ['datetime', 'date']
		('float', [])    : 16 | ['shift_1h', 'shift_7', 'shift_1', 'rollind_24h_std', 'shift_5h', ...]
		('int', [])      :  1 | ['humidity']
	Types of features in processed data (raw dtype, special dtypes):
		('category', [])             :  5 | ['hour', 'weather', 'weekday', 'day', 'month']
		('float', [])                : 16 | ['shift_1h', 'shift_7', 'shift_1', 'rollind_24h_std', 'shift_5h', ...]
		('int', [])                  :  1 | ['humidity']
		('int', ['bool'])            :  1 | ['workingday']
		('int', ['datetime_as_int']) :  2 | ['datetime', 'date']
	0.2s = Fit runtime
	25 features in original data used to generate 25 features in processed data.
	Train Data (Processed) Memory Usage: 1.72 MB (0.1% of available memory)
Data preprocessing and feature engineering runtime = 0.29s ...
AutoGluon will gauge predictive performance using evaluation metric: 'mean_squared_log_error'
	To change this, specify the eval_metric argument of fit()
AutoGluon will fit 2 stack levels (L1 to L2) ...
Fitting 11 L1 models ...
Hyperparameter tuning model: KNeighborsUnif_BAG_L1 ...
Fitted model: KNeighborsUnif_BAG_L1\T0 ...
	-0.1282	 = Validation score   (mean_squared_log_error)
	0.01s	 = Training   runtime
	4.55s	 = Validation runtime
Hyperparameter tuning model: KNeighborsDist_BAG_L1 ...
Fitted model: KNeighborsDist_BAG_L1\T0 ...
	-0.1254	 = Validation score   (mean_squared_log_error)
	0.01s	 = Training   runtime
	4.57s	 = Validation runtime
Hyperparameter tuning model: LightGBMXT_BAG_L1 ...
Warning: Exception caused LightGBMXT_BAG_L1 to fail during hyperparameter tuning... Skipping this model.
Traceback (most recent call last):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\tabular\trainer\abstract_trainer.py", line 1153, in _train_single_full
    hpo_models, hpo_model_performances, hpo_results = model.hyperparameter_tune(X=X, y=y, k_fold=k_fold, scheduler_options=hyperparameter_tune_kwargs, **model_fit_kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 830, in hyperparameter_tune
    return self._hyperparameter_tune(scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\stacker_ensemble_model.py", line 179, in _hyperparameter_tune
    return super()._hyperparameter_tune(X=X, y=y, k_fold=k_fold, scheduler_options=scheduler_options, preprocess_kwargs=preprocess_kwargs, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 801, in _hyperparameter_tune
    hpo_models, hpo_model_performances, hpo_results = self.model_base.hyperparameter_tune(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 830, in hyperparameter_tune
    return self._hyperparameter_tune(scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\tabular\models\lgb\lgb_model.py", line 391, in _hyperparameter_tune
    scheduler.run()
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\scheduler\seq_scheduler.py", line 152, in run
    for i in (tqdm(r) if self.num_trials < 1000 else r):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 242, in __init__
    self.container = self.status_printer(self.fp, total, self.desc, self.ncols)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 116, in status_printer
    "IProgress not found. Please update jupyter and ipywidgets."
ImportError: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
Exception ignored in: <bound method tqdm.__del__ of <tqdm.auto.tqdm object at 0x000002977FEFDA20>>
Traceback (most recent call last):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\std.py", line 1147, in __del__
    self.close()
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 286, in close
    self.disp(bar_style='danger', check_delay=False)
AttributeError: 'tqdm' object has no attribute 'disp'
Hyperparameter tuning model: LightGBM_BAG_L1 ...
Warning: Exception caused LightGBM_BAG_L1 to fail during hyperparameter tuning... Skipping this model.
Traceback (most recent call last):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\tabular\trainer\abstract_trainer.py", line 1153, in _train_single_full
    hpo_models, hpo_model_performances, hpo_results = model.hyperparameter_tune(X=X, y=y, k_fold=k_fold, scheduler_options=hyperparameter_tune_kwargs, **model_fit_kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 830, in hyperparameter_tune
    return self._hyperparameter_tune(scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\stacker_ensemble_model.py", line 179, in _hyperparameter_tune
    return super()._hyperparameter_tune(X=X, y=y, k_fold=k_fold, scheduler_options=scheduler_options, preprocess_kwargs=preprocess_kwargs, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 801, in _hyperparameter_tune
    hpo_models, hpo_model_performances, hpo_results = self.model_base.hyperparameter_tune(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 830, in hyperparameter_tune
    return self._hyperparameter_tune(scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\tabular\models\lgb\lgb_model.py", line 391, in _hyperparameter_tune
    scheduler.run()
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\scheduler\seq_scheduler.py", line 152, in run
    for i in (tqdm(r) if self.num_trials < 1000 else r):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 242, in __init__
    self.container = self.status_printer(self.fp, total, self.desc, self.ncols)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 116, in status_printer
    "IProgress not found. Please update jupyter and ipywidgets."
ImportError: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
Exception ignored in: <bound method tqdm.__del__ of <tqdm.auto.tqdm object at 0x00000297800215F8>>
Traceback (most recent call last):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\std.py", line 1147, in __del__
    self.close()
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 286, in close
    self.disp(bar_style='danger', check_delay=False)
AttributeError: 'tqdm' object has no attribute 'disp'
Hyperparameter tuning model: RandomForestMSE_BAG_L1 ...
Fitted model: RandomForestMSE_BAG_L1\T0 ...
	-0.0881	 = Validation score   (mean_squared_log_error)
	12.57s	 = Training   runtime
	0.57s	 = Validation runtime
Hyperparameter tuning model: CatBoost_BAG_L1 ...
Warning: Exception caused CatBoost_BAG_L1 to fail during hyperparameter tuning... Skipping this model.
Traceback (most recent call last):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\tabular\trainer\abstract_trainer.py", line 1153, in _train_single_full
    hpo_models, hpo_model_performances, hpo_results = model.hyperparameter_tune(X=X, y=y, k_fold=k_fold, scheduler_options=hyperparameter_tune_kwargs, **model_fit_kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 830, in hyperparameter_tune
    return self._hyperparameter_tune(scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\stacker_ensemble_model.py", line 179, in _hyperparameter_tune
    return super()._hyperparameter_tune(X=X, y=y, k_fold=k_fold, scheduler_options=scheduler_options, preprocess_kwargs=preprocess_kwargs, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 801, in _hyperparameter_tune
    hpo_models, hpo_model_performances, hpo_results = self.model_base.hyperparameter_tune(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 830, in hyperparameter_tune
    return self._hyperparameter_tune(scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 887, in _hyperparameter_tune
    scheduler.run()
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\scheduler\seq_scheduler.py", line 152, in run
    for i in (tqdm(r) if self.num_trials < 1000 else r):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 242, in __init__
    self.container = self.status_printer(self.fp, total, self.desc, self.ncols)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 116, in status_printer
    "IProgress not found. Please update jupyter and ipywidgets."
ImportError: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
Exception ignored in: <bound method tqdm.__del__ of <tqdm.auto.tqdm object at 0x0000029780012710>>
Traceback (most recent call last):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\std.py", line 1147, in __del__
    self.close()
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 286, in close
    self.disp(bar_style='danger', check_delay=False)
AttributeError: 'tqdm' object has no attribute 'disp'
Hyperparameter tuning model: ExtraTreesMSE_BAG_L1 ...
Fitted model: ExtraTreesMSE_BAG_L1\T0 ...
	-0.0862	 = Validation score   (mean_squared_log_error)
	3.89s	 = Training   runtime
	0.64s	 = Validation runtime
Hyperparameter tuning model: NeuralNetFastAI_BAG_L1 ...
Metric mean_squared_log_error is not supported by this model - using mean_squared_error instead
	Ran out of time, stopping training early. (Stopping on epoch 8)
Warning: Exception caused NeuralNetFastAI_BAG_L1 to fail during hyperparameter tuning... Skipping this model.
Traceback (most recent call last):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\tabular\trainer\abstract_trainer.py", line 1153, in _train_single_full
    hpo_models, hpo_model_performances, hpo_results = model.hyperparameter_tune(X=X, y=y, k_fold=k_fold, scheduler_options=hyperparameter_tune_kwargs, **model_fit_kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 830, in hyperparameter_tune
    return self._hyperparameter_tune(scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\stacker_ensemble_model.py", line 179, in _hyperparameter_tune
    return super()._hyperparameter_tune(X=X, y=y, k_fold=k_fold, scheduler_options=scheduler_options, preprocess_kwargs=preprocess_kwargs, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 801, in _hyperparameter_tune
    hpo_models, hpo_model_performances, hpo_results = self.model_base.hyperparameter_tune(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 830, in hyperparameter_tune
    return self._hyperparameter_tune(scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\tabular\models\fastainn\tabular_nn_fastai.py", line 394, in _hyperparameter_tune
    return skip_hpo(self, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\model_trial.py", line 98, in skip_hpo
    fit_and_save_model(model=model, params=dict(), fit_args=fit_model_args, predict_proba_args=predict_proba_args, y_val=y_val, time_start=time.time(), time_limit=time_limit)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\model_trial.py", line 86, in fit_and_save_model
    model.val_score = model.score_with_y_pred_proba(y=y_val, y_pred_proba=y_pred_proba, sample_weight=sample_weight_val)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 635, in score_with_y_pred_proba
    return compute_weighted_metric(y, y_pred, metric, sample_weight, quantile_levels=self.quantile_levels)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\utils\utils.py", line 568, in compute_weighted_metric
    return metric(y, y_pred, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\metrics\__init__.py", line 128, in __call__
    **self._kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\sklearn\utils\validation.py", line 63, in inner_f
    return f(*args, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\sklearn\metrics\_regression.py", line 413, in mean_squared_log_error
    raise ValueError("Mean Squared Logarithmic Error cannot be used when "
ValueError: Mean Squared Logarithmic Error cannot be used when targets contain negative values.
Mean Squared Logarithmic Error cannot be used when targets contain negative values.
Hyperparameter tuning model: XGBoost_BAG_L1 ...
Warning: Exception caused XGBoost_BAG_L1 to fail during hyperparameter tuning... Skipping this model.
Traceback (most recent call last):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\tabular\trainer\abstract_trainer.py", line 1153, in _train_single_full
    hpo_models, hpo_model_performances, hpo_results = model.hyperparameter_tune(X=X, y=y, k_fold=k_fold, scheduler_options=hyperparameter_tune_kwargs, **model_fit_kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 830, in hyperparameter_tune
    return self._hyperparameter_tune(scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\stacker_ensemble_model.py", line 179, in _hyperparameter_tune
    return super()._hyperparameter_tune(X=X, y=y, k_fold=k_fold, scheduler_options=scheduler_options, preprocess_kwargs=preprocess_kwargs, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 801, in _hyperparameter_tune
    hpo_models, hpo_model_performances, hpo_results = self.model_base.hyperparameter_tune(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 830, in hyperparameter_tune
    return self._hyperparameter_tune(scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 887, in _hyperparameter_tune
    scheduler.run()
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\scheduler\seq_scheduler.py", line 152, in run
    for i in (tqdm(r) if self.num_trials < 1000 else r):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 242, in __init__
    self.container = self.status_printer(self.fp, total, self.desc, self.ncols)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 116, in status_printer
    "IProgress not found. Please update jupyter and ipywidgets."
ImportError: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
Exception ignored in: <bound method tqdm.__del__ of <tqdm.auto.tqdm object at 0x0000029783591908>>
Traceback (most recent call last):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\std.py", line 1147, in __del__
    self.close()
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 286, in close
    self.disp(bar_style='danger', check_delay=False)
AttributeError: 'tqdm' object has no attribute 'disp'
Hyperparameter tuning model: NeuralNetMXNet_BAG_L1 ...
Warning: Exception caused NeuralNetMXNet_BAG_L1 to fail during hyperparameter tuning... Skipping this model.
Traceback (most recent call last):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\tabular\trainer\abstract_trainer.py", line 1153, in _train_single_full
    hpo_models, hpo_model_performances, hpo_results = model.hyperparameter_tune(X=X, y=y, k_fold=k_fold, scheduler_options=hyperparameter_tune_kwargs, **model_fit_kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 830, in hyperparameter_tune
    return self._hyperparameter_tune(scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\stacker_ensemble_model.py", line 179, in _hyperparameter_tune
    return super()._hyperparameter_tune(X=X, y=y, k_fold=k_fold, scheduler_options=scheduler_options, preprocess_kwargs=preprocess_kwargs, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 801, in _hyperparameter_tune
    hpo_models, hpo_model_performances, hpo_results = self.model_base.hyperparameter_tune(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 830, in hyperparameter_tune
    return self._hyperparameter_tune(scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\tabular\models\tabular_nn\tabular_nn_model.py", line 749, in _hyperparameter_tune
    scheduler.run()
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\scheduler\seq_scheduler.py", line 152, in run
    for i in (tqdm(r) if self.num_trials < 1000 else r):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 242, in __init__
    self.container = self.status_printer(self.fp, total, self.desc, self.ncols)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 116, in status_printer
    "IProgress not found. Please update jupyter and ipywidgets."
ImportError: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
Exception ignored in: <bound method tqdm.__del__ of <tqdm.auto.tqdm object at 0x000002978358B7F0>>
Traceback (most recent call last):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\std.py", line 1147, in __del__
    self.close()
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 286, in close
    self.disp(bar_style='danger', check_delay=False)
AttributeError: 'tqdm' object has no attribute 'disp'
Fitting model: LightGBMLarge_BAG_L1 ... Training model for up to 6.54s of the 1141.84s of remaining time.
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	Warning: Exception caused LightGBMLarge_BAG_L1 to fail during training... Skipping this model.
		Mean Squared Logarithmic Error cannot be used when targets contain negative values.
Detailed Traceback:
Traceback (most recent call last):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\tabular\trainer\abstract_trainer.py", line 962, in _train_and_save
    model = self._train_single(X, y, model, X_val, y_val, **model_fit_kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\tabular\trainer\abstract_trainer.py", line 934, in _train_single
    model = model.fit(X=X, y=y, X_val=X_val, y_val=y_val, **model_fit_kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 522, in fit
    out = self._fit(**kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\stacker_ensemble_model.py", line 153, in _fit
    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 189, in _fit
    n_repeats=n_repeats, n_repeat_start=n_repeat_start, save_folds=save_bag_folds, groups=groups, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 388, in _fit_folds
    fold_fitting_strategy.after_all_folds_scheduled()
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 59, in after_all_folds_scheduled
    self._fit_fold_model(*job)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 64, in _fit_fold_model
    fold_model = self._fit(model_base, time_start_fold, time_limit_fold, fold_ctx, kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 111, in _fit
    fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, time_limit=time_limit_fold, **kwargs_fold)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 522, in fit
    out = self._fit(**kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\tabular\models\lgb\lgb_model.py", line 182, in _fit
    self.model = lgb.train(**train_params)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py", line 299, in train
    evaluation_result_list.extend(booster.eval_valid(feval))
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\basic.py", line 3271, in eval_valid
    return [item for i in range(1, self.__num_dataset)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\basic.py", line 3272, in <listcomp>
    for item in self.__inner_eval(self.name_valid_sets[i - 1], i, feval)]
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\basic.py", line 3809, in __inner_eval
    feval_ret = eval_function(self.__inner_predict(data_idx), cur_data)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\tabular\models\lgb\lgb_utils.py", line 62, in function_template
    return metric.name, metric(y_true, y_hat), is_higher_better
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\metrics\__init__.py", line 128, in __call__
    **self._kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\sklearn\utils\validation.py", line 63, in inner_f
    return f(*args, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\sklearn\metrics\_regression.py", line 413, in mean_squared_log_error
    raise ValueError("Mean Squared Logarithmic Error cannot be used when "
ValueError: Mean Squared Logarithmic Error cannot be used when targets contain negative values.
Fitting model: KNeighborsUnif_BAG_L1\T0 ... Training model for up to 738.84s of the 1138.94s of remaining time.
	-0.1282	 = Validation score   (mean_squared_log_error)
	0.01s	 = Training   runtime
	4.55s	 = Validation runtime
Fitting model: KNeighborsDist_BAG_L1\T0 ... Training model for up to 738.82s of the 1138.92s of remaining time.
	-0.1254	 = Validation score   (mean_squared_log_error)
	0.01s	 = Training   runtime
	4.57s	 = Validation runtime
Fitting model: RandomForestMSE_BAG_L1\T0 ... Training model for up to 738.81s of the 1138.91s of remaining time.
	-0.0881	 = Validation score   (mean_squared_log_error)
	12.57s	 = Training   runtime
	0.57s	 = Validation runtime
Fitting model: ExtraTreesMSE_BAG_L1\T0 ... Training model for up to 738.79s of the 1138.89s of remaining time.
	-0.0862	 = Validation score   (mean_squared_log_error)
	3.89s	 = Training   runtime
	0.64s	 = Validation runtime
Repeating k-fold bagging: 2/20
Repeating k-fold bagging: 3/20
Repeating k-fold bagging: 4/20
Repeating k-fold bagging: 5/20
Repeating k-fold bagging: 6/20
Repeating k-fold bagging: 7/20
Repeating k-fold bagging: 8/20
Repeating k-fold bagging: 9/20
Repeating k-fold bagging: 10/20
Repeating k-fold bagging: 11/20
Repeating k-fold bagging: 12/20
Repeating k-fold bagging: 13/20
Repeating k-fold bagging: 14/20
Repeating k-fold bagging: 15/20
Repeating k-fold bagging: 16/20
Repeating k-fold bagging: 17/20
Repeating k-fold bagging: 18/20
Repeating k-fold bagging: 19/20
Repeating k-fold bagging: 20/20
Completed 20/20 k-fold bagging repeats ...
Fitting model: WeightedEnsemble_L2 ... Training model for up to 360.0s of the 1138.81s of remaining time.
	-0.0856	 = Validation score   (mean_squared_log_error)
	0.4s	 = Training   runtime
	0.0s	 = Validation runtime
Fitting 9 L2 models ...
Hyperparameter tuning model: LightGBMXT_BAG_L2 ...
Warning: Exception caused LightGBMXT_BAG_L2 to fail during hyperparameter tuning... Skipping this model.
Traceback (most recent call last):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\tabular\trainer\abstract_trainer.py", line 1153, in _train_single_full
    hpo_models, hpo_model_performances, hpo_results = model.hyperparameter_tune(X=X, y=y, k_fold=k_fold, scheduler_options=hyperparameter_tune_kwargs, **model_fit_kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 830, in hyperparameter_tune
    return self._hyperparameter_tune(scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\stacker_ensemble_model.py", line 179, in _hyperparameter_tune
    return super()._hyperparameter_tune(X=X, y=y, k_fold=k_fold, scheduler_options=scheduler_options, preprocess_kwargs=preprocess_kwargs, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 801, in _hyperparameter_tune
    hpo_models, hpo_model_performances, hpo_results = self.model_base.hyperparameter_tune(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 830, in hyperparameter_tune
    return self._hyperparameter_tune(scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\tabular\models\lgb\lgb_model.py", line 391, in _hyperparameter_tune
    scheduler.run()
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\scheduler\seq_scheduler.py", line 152, in run
    for i in (tqdm(r) if self.num_trials < 1000 else r):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 242, in __init__
    self.container = self.status_printer(self.fp, total, self.desc, self.ncols)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 116, in status_printer
    "IProgress not found. Please update jupyter and ipywidgets."
ImportError: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
Exception ignored in: <bound method tqdm.__del__ of <tqdm.auto.tqdm object at 0x000002978004CF98>>
Traceback (most recent call last):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\std.py", line 1147, in __del__
    self.close()
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 286, in close
    self.disp(bar_style='danger', check_delay=False)
AttributeError: 'tqdm' object has no attribute 'disp'
Hyperparameter tuning model: LightGBM_BAG_L2 ...
Warning: Exception caused LightGBM_BAG_L2 to fail during hyperparameter tuning... Skipping this model.
Traceback (most recent call last):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\tabular\trainer\abstract_trainer.py", line 1153, in _train_single_full
    hpo_models, hpo_model_performances, hpo_results = model.hyperparameter_tune(X=X, y=y, k_fold=k_fold, scheduler_options=hyperparameter_tune_kwargs, **model_fit_kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 830, in hyperparameter_tune
    return self._hyperparameter_tune(scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\stacker_ensemble_model.py", line 179, in _hyperparameter_tune
    return super()._hyperparameter_tune(X=X, y=y, k_fold=k_fold, scheduler_options=scheduler_options, preprocess_kwargs=preprocess_kwargs, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 801, in _hyperparameter_tune
    hpo_models, hpo_model_performances, hpo_results = self.model_base.hyperparameter_tune(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 830, in hyperparameter_tune
    return self._hyperparameter_tune(scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\tabular\models\lgb\lgb_model.py", line 391, in _hyperparameter_tune
    scheduler.run()
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\scheduler\seq_scheduler.py", line 152, in run
    for i in (tqdm(r) if self.num_trials < 1000 else r):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 242, in __init__
    self.container = self.status_printer(self.fp, total, self.desc, self.ncols)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 116, in status_printer
    "IProgress not found. Please update jupyter and ipywidgets."
ImportError: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
Exception ignored in: <bound method tqdm.__del__ of <tqdm.auto.tqdm object at 0x000002977FE7E9B0>>
Traceback (most recent call last):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\std.py", line 1147, in __del__
    self.close()
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 286, in close
    self.disp(bar_style='danger', check_delay=False)
AttributeError: 'tqdm' object has no attribute 'disp'
Hyperparameter tuning model: RandomForestMSE_BAG_L2 ...
Fitted model: RandomForestMSE_BAG_L2\T0 ...
	-0.08	 = Validation score   (mean_squared_log_error)
	16.28s	 = Training   runtime
	0.61s	 = Validation runtime
Hyperparameter tuning model: CatBoost_BAG_L2 ...
Warning: Exception caused CatBoost_BAG_L2 to fail during hyperparameter tuning... Skipping this model.
Traceback (most recent call last):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\tabular\trainer\abstract_trainer.py", line 1153, in _train_single_full
    hpo_models, hpo_model_performances, hpo_results = model.hyperparameter_tune(X=X, y=y, k_fold=k_fold, scheduler_options=hyperparameter_tune_kwargs, **model_fit_kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 830, in hyperparameter_tune
    return self._hyperparameter_tune(scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\stacker_ensemble_model.py", line 179, in _hyperparameter_tune
    return super()._hyperparameter_tune(X=X, y=y, k_fold=k_fold, scheduler_options=scheduler_options, preprocess_kwargs=preprocess_kwargs, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 801, in _hyperparameter_tune
    hpo_models, hpo_model_performances, hpo_results = self.model_base.hyperparameter_tune(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 830, in hyperparameter_tune
    return self._hyperparameter_tune(scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 887, in _hyperparameter_tune
    scheduler.run()
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\scheduler\seq_scheduler.py", line 152, in run
    for i in (tqdm(r) if self.num_trials < 1000 else r):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 242, in __init__
    self.container = self.status_printer(self.fp, total, self.desc, self.ncols)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 116, in status_printer
    "IProgress not found. Please update jupyter and ipywidgets."
ImportError: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
Exception ignored in: <bound method tqdm.__del__ of <tqdm.auto.tqdm object at 0x0000029780087400>>
Traceback (most recent call last):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\std.py", line 1147, in __del__
    self.close()
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 286, in close
    self.disp(bar_style='danger', check_delay=False)
AttributeError: 'tqdm' object has no attribute 'disp'
Hyperparameter tuning model: ExtraTreesMSE_BAG_L2 ...
Fitted model: ExtraTreesMSE_BAG_L2\T0 ...
	-0.0792	 = Validation score   (mean_squared_log_error)
	4.23s	 = Training   runtime
	0.74s	 = Validation runtime
Hyperparameter tuning model: NeuralNetFastAI_BAG_L2 ...
Metric mean_squared_log_error is not supported by this model - using mean_squared_error instead
	Ran out of time, stopping training early. (Stopping on epoch 17)
Warning: Exception caused NeuralNetFastAI_BAG_L2 to fail during hyperparameter tuning... Skipping this model.
Traceback (most recent call last):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\tabular\trainer\abstract_trainer.py", line 1153, in _train_single_full
    hpo_models, hpo_model_performances, hpo_results = model.hyperparameter_tune(X=X, y=y, k_fold=k_fold, scheduler_options=hyperparameter_tune_kwargs, **model_fit_kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 830, in hyperparameter_tune
    return self._hyperparameter_tune(scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\stacker_ensemble_model.py", line 179, in _hyperparameter_tune
    return super()._hyperparameter_tune(X=X, y=y, k_fold=k_fold, scheduler_options=scheduler_options, preprocess_kwargs=preprocess_kwargs, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 801, in _hyperparameter_tune
    hpo_models, hpo_model_performances, hpo_results = self.model_base.hyperparameter_tune(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 830, in hyperparameter_tune
    return self._hyperparameter_tune(scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\tabular\models\fastainn\tabular_nn_fastai.py", line 394, in _hyperparameter_tune
    return skip_hpo(self, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\model_trial.py", line 98, in skip_hpo
    fit_and_save_model(model=model, params=dict(), fit_args=fit_model_args, predict_proba_args=predict_proba_args, y_val=y_val, time_start=time.time(), time_limit=time_limit)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\model_trial.py", line 86, in fit_and_save_model
    model.val_score = model.score_with_y_pred_proba(y=y_val, y_pred_proba=y_pred_proba, sample_weight=sample_weight_val)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 635, in score_with_y_pred_proba
    return compute_weighted_metric(y, y_pred, metric, sample_weight, quantile_levels=self.quantile_levels)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\utils\utils.py", line 568, in compute_weighted_metric
    return metric(y, y_pred, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\metrics\__init__.py", line 128, in __call__
    **self._kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\sklearn\utils\validation.py", line 63, in inner_f
    return f(*args, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\sklearn\metrics\_regression.py", line 413, in mean_squared_log_error
    raise ValueError("Mean Squared Logarithmic Error cannot be used when "
ValueError: Mean Squared Logarithmic Error cannot be used when targets contain negative values.
Mean Squared Logarithmic Error cannot be used when targets contain negative values.
Hyperparameter tuning model: XGBoost_BAG_L2 ...
Warning: Exception caused XGBoost_BAG_L2 to fail during hyperparameter tuning... Skipping this model.
Traceback (most recent call last):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\tabular\trainer\abstract_trainer.py", line 1153, in _train_single_full
    hpo_models, hpo_model_performances, hpo_results = model.hyperparameter_tune(X=X, y=y, k_fold=k_fold, scheduler_options=hyperparameter_tune_kwargs, **model_fit_kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 830, in hyperparameter_tune
    return self._hyperparameter_tune(scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\stacker_ensemble_model.py", line 179, in _hyperparameter_tune
    return super()._hyperparameter_tune(X=X, y=y, k_fold=k_fold, scheduler_options=scheduler_options, preprocess_kwargs=preprocess_kwargs, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 801, in _hyperparameter_tune
    hpo_models, hpo_model_performances, hpo_results = self.model_base.hyperparameter_tune(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 830, in hyperparameter_tune
    return self._hyperparameter_tune(scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 887, in _hyperparameter_tune
    scheduler.run()
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\scheduler\seq_scheduler.py", line 152, in run
    for i in (tqdm(r) if self.num_trials < 1000 else r):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 242, in __init__
    self.container = self.status_printer(self.fp, total, self.desc, self.ncols)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 116, in status_printer
    "IProgress not found. Please update jupyter and ipywidgets."
ImportError: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
Exception ignored in: <bound method tqdm.__del__ of <tqdm.auto.tqdm object at 0x00000297800879B0>>
Traceback (most recent call last):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\std.py", line 1147, in __del__
    self.close()
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 286, in close
    self.disp(bar_style='danger', check_delay=False)
AttributeError: 'tqdm' object has no attribute 'disp'
Hyperparameter tuning model: NeuralNetMXNet_BAG_L2 ...
Warning: Exception caused NeuralNetMXNet_BAG_L2 to fail during hyperparameter tuning... Skipping this model.
Traceback (most recent call last):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\tabular\trainer\abstract_trainer.py", line 1153, in _train_single_full
    hpo_models, hpo_model_performances, hpo_results = model.hyperparameter_tune(X=X, y=y, k_fold=k_fold, scheduler_options=hyperparameter_tune_kwargs, **model_fit_kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 830, in hyperparameter_tune
    return self._hyperparameter_tune(scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\stacker_ensemble_model.py", line 179, in _hyperparameter_tune
    return super()._hyperparameter_tune(X=X, y=y, k_fold=k_fold, scheduler_options=scheduler_options, preprocess_kwargs=preprocess_kwargs, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 801, in _hyperparameter_tune
    hpo_models, hpo_model_performances, hpo_results = self.model_base.hyperparameter_tune(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 830, in hyperparameter_tune
    return self._hyperparameter_tune(scheduler_options=scheduler_options, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\tabular\models\tabular_nn\tabular_nn_model.py", line 749, in _hyperparameter_tune
    scheduler.run()
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\scheduler\seq_scheduler.py", line 152, in run
    for i in (tqdm(r) if self.num_trials < 1000 else r):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 242, in __init__
    self.container = self.status_printer(self.fp, total, self.desc, self.ncols)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 116, in status_printer
    "IProgress not found. Please update jupyter and ipywidgets."
ImportError: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
Exception ignored in: <bound method tqdm.__del__ of <tqdm.auto.tqdm object at 0x00000297835F1390>>
Traceback (most recent call last):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\std.py", line 1147, in __del__
    self.close()
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\tqdm\notebook.py", line 286, in close
    self.disp(bar_style='danger', check_delay=False)
AttributeError: 'tqdm' object has no attribute 'disp'
Fitting model: LightGBMLarge_BAG_L2 ... Training model for up to 11.38s of the 1091.53s of remaining time.
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	-0.0645	 = Validation score   (mean_squared_log_error)
	5.52s	 = Training   runtime
	0.03s	 = Validation runtime
Fitting model: RandomForestMSE_BAG_L2\T0 ... Training model for up to 1085.81s of the 1085.8s of remaining time.
	-0.08	 = Validation score   (mean_squared_log_error)
	16.28s	 = Training   runtime
	0.61s	 = Validation runtime
Fitting model: ExtraTreesMSE_BAG_L2\T0 ... Training model for up to 1085.79s of the 1085.78s of remaining time.
	-0.0792	 = Validation score   (mean_squared_log_error)
	4.23s	 = Training   runtime
	0.74s	 = Validation runtime
Fitting model: LightGBMLarge_BAG_L2 ... Training model for up to 1085.76s of the 1085.76s of remaining time.
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
	Warning: Exception caused LightGBMLarge_BAG_L2 to fail during training... Skipping this model.
		Mean Squared Logarithmic Error cannot be used when targets contain negative values.
Detailed Traceback:
Traceback (most recent call last):
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\tabular\trainer\abstract_trainer.py", line 962, in _train_and_save
    model = self._train_single(X, y, model, X_val, y_val, **model_fit_kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\tabular\trainer\abstract_trainer.py", line 934, in _train_single
    model = model.fit(X=X, y=y, X_val=X_val, y_val=y_val, **model_fit_kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 522, in fit
    out = self._fit(**kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\stacker_ensemble_model.py", line 153, in _fit
    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 189, in _fit
    n_repeats=n_repeats, n_repeat_start=n_repeat_start, save_folds=save_bag_folds, groups=groups, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 388, in _fit_folds
    fold_fitting_strategy.after_all_folds_scheduled()
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 59, in after_all_folds_scheduled
    self._fit_fold_model(*job)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 64, in _fit_fold_model
    fold_model = self._fit(model_base, time_start_fold, time_limit_fold, fold_ctx, kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\ensemble\fold_fitting_strategy.py", line 111, in _fit
    fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, time_limit=time_limit_fold, **kwargs_fold)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\models\abstract\abstract_model.py", line 522, in fit
    out = self._fit(**kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\tabular\models\lgb\lgb_model.py", line 182, in _fit
    self.model = lgb.train(**train_params)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py", line 299, in train
    evaluation_result_list.extend(booster.eval_valid(feval))
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\basic.py", line 3271, in eval_valid
    return [item for i in range(1, self.__num_dataset)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\basic.py", line 3272, in <listcomp>
    for item in self.__inner_eval(self.name_valid_sets[i - 1], i, feval)]
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\basic.py", line 3809, in __inner_eval
    feval_ret = eval_function(self.__inner_predict(data_idx), cur_data)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\tabular\models\lgb\lgb_utils.py", line 62, in function_template
    return metric.name, metric(y_true, y_hat), is_higher_better
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\autogluon\core\metrics\__init__.py", line 128, in __call__
    **self._kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\sklearn\utils\validation.py", line 63, in inner_f
    return f(*args, **kwargs)
  File "C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\sklearn\metrics\_regression.py", line 413, in mean_squared_log_error
    raise ValueError("Mean Squared Logarithmic Error cannot be used when "
ValueError: Mean Squared Logarithmic Error cannot be used when targets contain negative values.
Repeating k-fold bagging: 2/20
Repeating k-fold bagging: 3/20
Repeating k-fold bagging: 4/20
Repeating k-fold bagging: 5/20
Repeating k-fold bagging: 6/20
Repeating k-fold bagging: 7/20
Repeating k-fold bagging: 8/20
Repeating k-fold bagging: 9/20
Repeating k-fold bagging: 10/20
Repeating k-fold bagging: 11/20
Repeating k-fold bagging: 12/20
Repeating k-fold bagging: 13/20
Repeating k-fold bagging: 14/20
Repeating k-fold bagging: 15/20
Repeating k-fold bagging: 16/20
Repeating k-fold bagging: 17/20
Repeating k-fold bagging: 18/20
Repeating k-fold bagging: 19/20
Repeating k-fold bagging: 20/20
Completed 20/20 k-fold bagging repeats ...
Fitting model: WeightedEnsemble_L3 ... Training model for up to 360.0s of the 1062.15s of remaining time.
	-0.0788	 = Validation score   (mean_squared_log_error)
	0.23s	 = Training   runtime
	0.0s	 = Validation runtime
AutoGluon training complete, total runtime = 138.1s ...
TabularPredictor saved. To load, use: predictor = TabularPredictor.load("AutogluonModels/ag-20211210_143954\")

predictor_RMSLE.fit_summary()

*** Summary of fit() ***
Estimated performance of each model:
                       model  score_val  pred_time_val   fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order
0       LightGBMLarge_BAG_L2  -0.064508      10.361032  21.990339                0.031981           5.518822            2       True          8
1        WeightedEnsemble_L3  -0.078823      11.678289  37.214586                0.001013           0.229880            3       True          9
2    ExtraTreesMSE_BAG_L2\T0  -0.079187      11.066625  20.702082                0.737575           4.230565            2       True          7
3  RandomForestMSE_BAG_L2\T0  -0.079998      10.939701  32.754141                0.610651          16.282624            2       True          6
4        WeightedEnsemble_L2  -0.085556       5.775673  16.860297                0.001000           0.398775            2       True          5
5    ExtraTreesMSE_BAG_L1\T0  -0.086199       0.640632   3.886761                0.640632           3.886761            1       True          4
6  RandomForestMSE_BAG_L1\T0  -0.088100       0.566673  12.566764                0.566673          12.566764            1       True          3
7   KNeighborsDist_BAG_L1\T0  -0.125425       4.567369   0.007997                4.567369           0.007997            1       True          2
8   KNeighborsUnif_BAG_L1\T0  -0.128212       4.554377   0.009995                4.554377           0.009995            1       True          1
Number of models trained: 9
Types of models trained:
{'StackerEnsembleModel_LGB', 'StackerEnsembleModel_XT', 'StackerEnsembleModel_KNN', 'WeightedEnsembleModel', 'StackerEnsembleModel_RF'}
Bagging used: True  (with 10 folds)
Multi-layer stack-ensembling used: True  (with 3 levels)
Feature Metadata (Processed):
(raw dtype, special dtypes):
('category', [])             :  5 | ['hour', 'weather', 'weekday', 'day', 'month']
('float', [])                : 16 | ['shift_1h', 'shift_7', 'shift_1', 'rollind_24h_std', 'shift_5h', ...]
('int', [])                  :  1 | ['humidity']
('int', ['bool'])            :  1 | ['workingday']
('int', ['datetime_as_int']) :  2 | ['datetime', 'date']
Plot summary of models saved to file: AutogluonModels/ag-20211210_143954\SummaryOfModels.html
*** End of fit() summary ***

{'model_types': {'KNeighborsUnif_BAG_L1\\T0': 'StackerEnsembleModel_KNN',
  'KNeighborsDist_BAG_L1\\T0': 'StackerEnsembleModel_KNN',
  'RandomForestMSE_BAG_L1\\T0': 'StackerEnsembleModel_RF',
  'ExtraTreesMSE_BAG_L1\\T0': 'StackerEnsembleModel_XT',
  'WeightedEnsemble_L2': 'WeightedEnsembleModel',
  'RandomForestMSE_BAG_L2\\T0': 'StackerEnsembleModel_RF',
  'ExtraTreesMSE_BAG_L2\\T0': 'StackerEnsembleModel_XT',
  'LightGBMLarge_BAG_L2': 'StackerEnsembleModel_LGB',
  'WeightedEnsemble_L3': 'WeightedEnsembleModel'},
 'model_performance': {'KNeighborsUnif_BAG_L1\\T0': -0.1282124255277413,
  'KNeighborsDist_BAG_L1\\T0': -0.1254249071125922,
  'RandomForestMSE_BAG_L1\\T0': -0.08810034776923616,
  'ExtraTreesMSE_BAG_L1\\T0': -0.08619897379945887,
  'WeightedEnsemble_L2': -0.08555574945073402,
  'RandomForestMSE_BAG_L2\\T0': -0.07999814135236705,
  'ExtraTreesMSE_BAG_L2\\T0': -0.07918714277968329,
  'LightGBMLarge_BAG_L2': -0.06450836120910762,
  'WeightedEnsemble_L3': -0.0788231719230093},
 'model_best': 'WeightedEnsemble_L3',
 'model_paths': {'KNeighborsUnif_BAG_L1\\T0': 'AutogluonModels/ag-20211210_143954\\models\\KNeighborsUnif_BAG_L1\\T0\\',
  'KNeighborsDist_BAG_L1\\T0': 'AutogluonModels/ag-20211210_143954\\models\\KNeighborsDist_BAG_L1\\T0\\',
  'RandomForestMSE_BAG_L1\\T0': 'AutogluonModels/ag-20211210_143954\\models\\RandomForestMSE_BAG_L1\\T0\\',
  'ExtraTreesMSE_BAG_L1\\T0': 'AutogluonModels/ag-20211210_143954\\models\\ExtraTreesMSE_BAG_L1\\T0\\',
  'WeightedEnsemble_L2': 'AutogluonModels/ag-20211210_143954\\models\\WeightedEnsemble_L2\\',
  'RandomForestMSE_BAG_L2\\T0': 'AutogluonModels/ag-20211210_143954\\models\\RandomForestMSE_BAG_L2\\T0\\',
  'ExtraTreesMSE_BAG_L2\\T0': 'AutogluonModels/ag-20211210_143954\\models\\ExtraTreesMSE_BAG_L2\\T0\\',
  'LightGBMLarge_BAG_L2': 'AutogluonModels/ag-20211210_143954\\models\\LightGBMLarge_BAG_L2\\',
  'WeightedEnsemble_L3': 'AutogluonModels/ag-20211210_143954\\models\\WeightedEnsemble_L3\\'},
 'model_fit_times': {'KNeighborsUnif_BAG_L1\\T0': 0.009995460510253906,
  'KNeighborsDist_BAG_L1\\T0': 0.007997274398803711,
  'RandomForestMSE_BAG_L1\\T0': 12.566763639450073,
  'ExtraTreesMSE_BAG_L1\\T0': 3.886760711669922,
  'WeightedEnsemble_L2': 0.3987753391265869,
  'RandomForestMSE_BAG_L2\\T0': 16.282623767852783,
  'ExtraTreesMSE_BAG_L2\\T0': 4.230564832687378,
  'LightGBMLarge_BAG_L2': 5.518822193145752,
  'WeightedEnsemble_L3': 0.22988033294677734},
 'model_pred_times': {'KNeighborsUnif_BAG_L1\\T0': 4.554376840591431,
  'KNeighborsDist_BAG_L1\\T0': 4.567368507385254,
  'RandomForestMSE_BAG_L1\\T0': 0.5666728019714355,
  'ExtraTreesMSE_BAG_L1\\T0': 0.6406323909759521,
  'WeightedEnsemble_L2': 0.0009996891021728516,
  'RandomForestMSE_BAG_L2\\T0': 0.6106507778167725,
  'ExtraTreesMSE_BAG_L2\\T0': 0.7375748157501221,
  'LightGBMLarge_BAG_L2': 0.03198122978210449,
  'WeightedEnsemble_L3': 0.0010128021240234375},
 'num_bag_folds': 10,
 'max_stack_level': 3,
 'model_hyperparams': {'KNeighborsUnif_BAG_L1\\T0': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True,
   'use_child_oof': True},
  'KNeighborsDist_BAG_L1\\T0': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True,
   'use_child_oof': True},
  'RandomForestMSE_BAG_L1\\T0': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True,
   'use_child_oof': True},
  'ExtraTreesMSE_BAG_L1\\T0': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True,
   'use_child_oof': True},
  'WeightedEnsemble_L2': {'use_orig_features': False,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'RandomForestMSE_BAG_L2\\T0': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True,
   'use_child_oof': True},
  'ExtraTreesMSE_BAG_L2\\T0': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True,
   'use_child_oof': True},
  'LightGBMLarge_BAG_L2': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'WeightedEnsemble_L3': {'use_orig_features': False,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True}},
 'leaderboard':                        model  score_val  pred_time_val   fit_time  \
 0       LightGBMLarge_BAG_L2  -0.064508      10.361032  21.990339   
 1        WeightedEnsemble_L3  -0.078823      11.678289  37.214586   
 2    ExtraTreesMSE_BAG_L2\T0  -0.079187      11.066625  20.702082   
 3  RandomForestMSE_BAG_L2\T0  -0.079998      10.939701  32.754141   
 4        WeightedEnsemble_L2  -0.085556       5.775673  16.860297   
 5    ExtraTreesMSE_BAG_L1\T0  -0.086199       0.640632   3.886761   
 6  RandomForestMSE_BAG_L1\T0  -0.088100       0.566673  12.566764   
 7   KNeighborsDist_BAG_L1\T0  -0.125425       4.567369   0.007997   
 8   KNeighborsUnif_BAG_L1\T0  -0.128212       4.554377   0.009995   
 
    pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  \
 0                0.031981           5.518822            2       True   
 1                0.001013           0.229880            3       True   
 2                0.737575           4.230565            2       True   
 3                0.610651          16.282624            2       True   
 4                0.001000           0.398775            2       True   
 5                0.640632           3.886761            1       True   
 6                0.566673          12.566764            1       True   
 7                4.567369           0.007997            1       True   
 8                4.554377           0.009995            1       True   
 
    fit_order  
 0          8  
 1          9  
 2          7  
 3          6  
 4          5  
 5          4  
 6          3  
 7          2  
 8          1  }

# Remember to set all negative values to zero
predictor_bag = predictor_RMSLE.predict(df_test)
predictor_bag[predictor_bag<0] = 0

# Same submitting predictions
submission_hypo = pd.read_csv('sampleSubmission.csv')
submission_hypo["count"] = predictor_bag
submission_hypo.to_csv("submission_submission_RMSLE.csv", index=False)

!kaggle competitions submit -c bike-sharing-demand -f submission_submission_RMSLE.csv -m "Custome Eval Metric"

Successfully submitted to Bike Sharing Demand

c:\users\hp\anaconda3\envs\aws  deep learning\lib\site-packages\cryptography\hazmat\backends\openssl\x509.py:17: CryptographyDeprecationWarning: This version of cryptography contains a temporary pyOpenSSL fallback path. Upgrade pyOpenSSL now.
  utils.DeprecatedIn35,

  0%|          | 0.00/194k [00:00<?, ?B/s]
  4%|4         | 8.00k/194k [00:00<00:03, 51.4kB/s]
 49%|####9     | 96.0k/194k [00:00<00:00, 430kB/s] 
100%|##########| 194k/194k [00:00<00:00, 641kB/s] 
100%|##########| 194k/194k [00:03<00:00, 57.2kB/s]

!kaggle competitions submissions -c bike-sharing-demand

fileName                                 date                 description                                   status    publicScore  privateScore  
---------------------------------------  -------------------  --------------------------------------------  --------  -----------  ------------  
submission_submission_RMSLE.csv          2021-12-10 14:44:00  Custome Eval Metric                           complete  0.45892      0.45892       
submission_submission_hypov2.csv         2021-12-10 14:23:31  new features with hyperparameters V2          complete  0.45490      0.45490       
submission_submission_hypov2.csv         2021-12-10 13:57:06  new features with hyperparameters V2          complete  0.57647      0.57647       
submission_submission_hypo.csv           2021-12-10 10:07:22  new features with hyperparameters             complete  0.45563      0.45563       
submission_time_search_5min.csv          2021-12-10 09:23:07  5 min train submit                            complete  0.44797      0.44797       
submission_import_features.csv           2021-12-08 13:55:39  Feature Importance                            complete  0.45194      0.45194       
submission_import_features.csv           2021-12-08 13:53:11  Feature Importance                            complete  0.45414      0.45414       
submission_import_features.csv           2021-12-08 13:51:11  Feature Importance                            complete  0.45749      0.45749       
submission_import_features.csv           2021-12-08 13:48:53  Feature Importance                            complete  0.45210      0.45210       
submission_import_features.csv           2021-12-08 13:44:55  Feature Importance                            complete  0.48275      0.48275       
submission_import_features.csv           2021-12-08 13:42:28  Feature Importance                            complete  0.45753      0.45753       
submission_import_features.csv           2021-12-08 13:39:01  Feature Importance                            complete  0.45939      0.45939       
submission_new_featuresV3.csv            2021-12-08 12:44:41  new features_AdvancedV3                       complete  0.45274      0.45274       
submission_new_features_Advanced_V1.csv  2021-12-08 12:20:43  new features Advanced V1                      complete  0.45810      0.45810       
submission_new_features_Advanced_V1.csv  2021-12-08 12:20:12  new features                                  complete  0.45810      0.45810       
submission_new_features_log.csv          2021-12-08 12:07:08  NF - Model 2  (Basic  Log Transform )         complete  0.50789      0.50789       
submission_new_features_log.csv          2021-12-08 12:06:06  NF - Model 2  (Basic  Log Transform )         complete  0.50674      0.50674       
submission_new_features_log.csv          2021-12-08 11:34:53  NF - Model 2  (Basic  Log Transform )         complete  0.50674      0.50674       
submission_new_features.csv              2021-12-08 11:24:55  NF - Model 1  (Basic  Time Catogricla data )  complete  0.50674      0.50674       
submission.csv                           2021-12-08 11:13:27  first raw submission                          complete  1.38952      1.38952       
submission_new_hpo.csv                   2021-12-08 11:06:33  new features with hyperparameters Tune        complete  0.48748      0.48748       
submission_new_hpo.csv                   2021-12-08 09:14:10  new features with hyperparameters Tune        complete  0.51858      0.51858       
submission_new_hpo_ensV4.csv             2021-12-07 15:02:09  new features with hyperparameters Vקרדןםמ 4   complete  0.48961      0.48961       
submission_new_hpo.csv                   2021-12-07 14:29:38  new features with hyperparameters Tune        complete  0.48338      0.48338       
submission_new_featuresV3.csv            2021-12-07 14:00:06  new featuresV3                                complete  0.45074      0.45074       
submission_new_featuresV2.csv            2021-12-07 13:39:45  new features                                  complete  0.50306      0.50306       
submission_new_features.csv              2021-12-07 13:09:48  new features                                  complete  0.51097      0.51097       
submission.csv                           2021-12-07 13:04:23  first raw submission                          complete  1.39309      1.39309       
submission_new_hpo.csv                   2021-12-07 11:12:13  new features with hyperparameters Tune        complete  0.45509      0.45509       
submission_new_featuresV3.csv            2021-12-07 10:53:43  new featuresV3                                complete  0.45057      0.45057       
submission_new_featuresV2.csv            2021-12-07 10:43:05  new features                                  complete  0.47595      0.47595       
submission_new_featuresV2.csv            2021-12-07 10:42:32  new features                                  error     None         None          
submission_new_features.csv              2021-12-07 10:32:26  new features                                  complete  0.50670      0.50670       
submission.csv                           2021-12-07 10:24:27  first raw submission                          complete  1.38989      1.38989       
submission_new_hpo.csv                   2021-12-07 09:08:34  new features with hyperparameters             complete  0.46172      0.46172       
submission_new_hpo.csv                   2021-12-07 08:59:07  new features with hyperparameters             complete  0.45760      0.45760       
submission_new_features.csv              2021-12-06 20:57:01  new features                                  complete  0.44755      0.44755       
submission_new_features.csv              2021-12-06 20:50:18  new features                                  complete  0.44577      0.44577       
submission_new_features.csv              2021-12-06 20:45:43  new features                                  complete  0.44592      0.44592       
submission_RMSLE.csv                     2021-12-06 17:57:57  Custome Eval Metric                           complete  0.46093      0.46093       
submission_RMSLE.csv                     2021-12-06 17:46:31  Custome Eval Metric                           complete  0.46093      0.46093       
submission_RMSLE.csv                     2021-12-06 17:25:04  Custome Eval Metric                           complete  0.46093      0.46093       
submission_RMSLE.csv                     2021-12-06 17:13:44  Custome Eval Metric                           complete  0.46093      0.46093       
submission_new_hpo_ens.csv               2021-12-06 15:59:53  new features with hyperparameters             complete  0.44685      0.44685       
submission_new_hpo_ens.csv               2021-12-06 15:55:19  new features with hyperparameters             complete  0.47243      0.47243       
submission_new_hpo_ens.csv               2021-12-06 15:14:56  new features with hyperparameters             complete  0.44685      0.44685       
submission_new_hpo_ens.csv               2021-12-06 15:11:35  new features with hyperparameters             complete  0.47219      0.47219       
submission_new_hpo_ens.csv               2021-12-06 15:03:25  new features with hyperparameters             complete  0.44685      0.44685       
submission_new_hpo.csv                   2021-12-06 15:02:29  new features with hyperparameters             complete  0.44685      0.44685       
submission_new_hpo.csv                   2021-12-06 13:32:40  new features with hyperparameters             complete  0.44575      0.44575

c:\users\hp\anaconda3\envs\aws  deep learning\lib\site-packages\cryptography\hazmat\backends\openssl\x509.py:17: CryptographyDeprecationWarning: This version of cryptography contains a temporary pyOpenSSL fallback path. Upgrade pyOpenSSL now.
  utils.DeprecatedIn35,

import autogluon.core as ag
from autogluon.tabular import TabularDataset, TabularPredictor

nn_options = {  # specifies non-default hyperparameter values for neural network models
    'num_epochs': 10,  # number of training epochs (controls training time of NN models)
    'learning_rate': ag.space.Real(1e-4, 1e-2, default=5e-4, log=True),  # learning rate used in training (real-valued hyperparameter searched on log-scale)
    'activation': ag.space.Categorical('relu', 'softrelu', 'tanh'),  # activation function used in NN (categorical hyperparameter, default = first entry)
    'layers': ag.space.Categorical([100], [1000], [200, 100], [300, 200, 100]),  # each choice for categorical hyperparameter 'layers' corresponds to list of sizes for each NN layer to use
    'dropout_prob': ag.space.Real(0.0, 0.5, default=0.1),  # dropout probability (real-valued hyperparameter)
}

gbm_options = {  # specifies non-default hyperparameter values for lightGBM gradient boosted trees
    'num_boost_round': 30000,  # number of boosting rounds (controls training time of GBM models)
    'num_leaves': ag.space.Int(lower=26, upper=126, default=36),  # number of leaves in trees (integer hyperparameter)
    'max_depth' : ag.space.Int(lower=3, upper=12, default=5),
    'min_samples_leaf '  : ag.space.Int(lower=12, upper=100, default=50)
}

hyperparameters = {  # hyperparameters of each model type
                   'GBM': gbm_options,
                  # 'NN': nn_options,  # NOTE: comment this line out if you get errors on Mac OSX
                  }  # When these keys are missing from hyperparameters dict, no models of that type are trained

time_limit = 35*60  # train various models for ~2 min
num_trials = 5  # try at most 5 different hyperparameter configurations for each type of model
search_strategy = 'auto'  # to tune hyperparameters using Bayesian optimization routine with a local scheduler

hyperparameter_tune_kwargs = {  # HPO is not performed unless hyperparameter_tune_kwargs is specified
    'num_trials': num_trials,
    'scheduler' : 'local',
    'searcher': search_strategy,
}

predictor = TabularPredictor(label='count', eval_metric='root_mean_squared_error').fit( train_data = df_features, time_limit=time_limit,   
    num_bag_folds=5, num_bag_sets=1, num_stack_levels=3,
    hyperparameter_tune_kwargs =  'auto',
    hyperparameters = { 'GBM': gbm_options}, 
    presets="best_quality",
)

No path specified. Models will be saved in: "AutogluonModels/ag-20211211_111356\"
Presets specified: ['best_quality']
Warning: hyperparameter tuning is currently experimental and may cause the process to hang.
Beginning AutoGluon training ... Time limit = 2100s
AutoGluon will save models to "AutogluonModels/ag-20211211_111356\"
AutoGluon Version:  0.3.1
Train Data Rows:    10886
Train Data Columns: 25
Preprocessing data ...
AutoGluon infers your prediction problem is: 'regression' (because dtype of label-column == float and many unique label-values observed).
	Label info (max, min, mean, stddev): (977.0, 1.0, 191.57413, 181.14445)
	If 'regression' is not the correct problem_type, please manually specify the problem_type argument in fit() (You may specify problem_type as one of: ['binary', 'multiclass', 'regression'])
Using Feature Generators to preprocess the data ...
Fitting AutoMLPipelineFeatureGenerator...
	Available Memory:                    3704.41 MB
	Train Data (Original)  Memory Usage: 1.72 MB (0.0% of available memory)
	Inferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.
	Stage 1 Generators:
		Fitting AsTypeFeatureGenerator...
			Note: Converting 1 features to boolean dtype as they only contain 2 unique values.
	Stage 2 Generators:
		Fitting FillNaFeatureGenerator...
	Stage 3 Generators:
		Fitting IdentityFeatureGenerator...
		Fitting CategoryFeatureGenerator...
			Fitting CategoryMemoryMinimizeFeatureGenerator...
		Fitting DatetimeFeatureGenerator...
	Stage 4 Generators:
		Fitting DropUniqueFeatureGenerator...
	Types of features in original data (raw dtype, special dtypes):
		('category', []) :  6 | ['hour', 'workingday', 'weather', 'weekday', 'day', ...]
		('datetime', []) :  2 | ['datetime', 'date']
		('float', [])    : 16 | ['shift_1h', 'shift_7', 'shift_1', 'rollind_24h_std', 'shift_5h', ...]
		('int', [])      :  1 | ['humidity']
	Types of features in processed data (raw dtype, special dtypes):
		('category', [])             :  5 | ['hour', 'weather', 'weekday', 'day', 'month']
		('float', [])                : 16 | ['shift_1h', 'shift_7', 'shift_1', 'rollind_24h_std', 'shift_5h', ...]
		('int', [])                  :  1 | ['humidity']
		('int', ['bool'])            :  1 | ['workingday']
		('int', ['datetime_as_int']) :  2 | ['datetime', 'date']
	0.2s = Fit runtime
	25 features in original data used to generate 25 features in processed data.
	Train Data (Processed) Memory Usage: 1.72 MB (0.0% of available memory)
Data preprocessing and feature engineering runtime = 0.28s ...
AutoGluon will gauge predictive performance using evaluation metric: 'root_mean_squared_error'
	To change this, specify the eval_metric argument of fit()
AutoGluon will fit 4 stack levels (L1 to L4) ...
Fitting 1 L1 models ...
Hyperparameter tuning model: LightGBM_BAG_L1 ...
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "

[LightGBM] [Warning] Unknown parameter: 76
[LightGBM] [Warning] min_data_in_leaf is set=13, min_samples_leaf= will be ignored. Current value: min_data_in_leaf=13

C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "

[LightGBM] [Warning] Unknown parameter: 35
[LightGBM] [Warning] min_data_in_leaf is set=19, min_samples_leaf= will be ignored. Current value: min_data_in_leaf=19
[1000]	train_set's rmse: 38.2065	valid_set's rmse: 41.9251
[2000]	train_set's rmse: 32.8062	valid_set's rmse: 37.843
[3000]	train_set's rmse: 29.9847	valid_set's rmse: 36.1912
[4000]	train_set's rmse: 28.1619	valid_set's rmse: 35.333
[5000]	train_set's rmse: 26.7378	valid_set's rmse: 34.7608
[6000]	train_set's rmse: 25.6698	valid_set's rmse: 34.5052
[7000]	train_set's rmse: 24.7708	valid_set's rmse: 34.302
[8000]	train_set's rmse: 23.9516	valid_set's rmse: 34.1244
[9000]	train_set's rmse: 23.1915	valid_set's rmse: 34.0434
[10000]	train_set's rmse: 22.5232	valid_set's rmse: 33.9074
[11000]	train_set's rmse: 21.9255	valid_set's rmse: 33.8516
[12000]	train_set's rmse: 21.3702	valid_set's rmse: 33.8266
[13000]	train_set's rmse: 20.8451	valid_set's rmse: 33.8152
[14000]	train_set's rmse: 20.3672	valid_set's rmse: 33.8133
[15000]	train_set's rmse: 19.9337	valid_set's rmse: 33.8168
[16000]	train_set's rmse: 19.5177	valid_set's rmse: 33.8447
[17000]	train_set's rmse: 19.129	valid_set's rmse: 33.8648

C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "

[LightGBM] [Warning] Unknown parameter: 100
[LightGBM] [Warning] min_data_in_leaf is set=18, min_samples_leaf= will be ignored. Current value: min_data_in_leaf=18

C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "

[LightGBM] [Warning] Unknown parameter: 100
[LightGBM] [Warning] min_data_in_leaf is set=27, min_samples_leaf= will be ignored. Current value: min_data_in_leaf=27

C:\Users\HP\Anaconda3\envs\AWS  Deep LEarning\lib\site-packages\lightgbm\engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "

[LightGBM] [Warning] Unknown parameter: 49
[LightGBM] [Warning] min_data_in_leaf is set=29, min_samples_leaf= will be ignored. Current value: min_data_in_leaf=29
[1000]	train_set's rmse: 35.6078	valid_set's rmse: 39.7816
[2000]	train_set's rmse: 30.6869	valid_set's rmse: 36.5645
[3000]	train_set's rmse: 28.1532	valid_set's rmse: 35.2956
[4000]	train_set's rmse: 26.4013	valid_set's rmse: 34.7255
[5000]	train_set's rmse: 25.0963	valid_set's rmse: 34.3984
[6000]	train_set's rmse: 24.0288	valid_set's rmse: 34.1353
[7000]	train_set's rmse: 23.1446	valid_set's rmse: 33.9787
[8000]	train_set's rmse: 22.2992	valid_set's rmse: 33.7657
[9000]	train_set's rmse: 21.5919	valid_set's rmse: 33.7567
[10000]	train_set's rmse: 20.9069	valid_set's rmse: 33.766

predictor.leaderboard(df_features, silent=True)

# Remember to set all negative values to zero
predictor_hp_v4 = predictor.predict(df_test)
predictor_hp_v4[predictor_hp_v4<0] = 0

# Same submitting predictions
submission_hypo = pd.read_csv('sampleSubmission.csv')
submission_hypo["count"] = predictor_hp_v4
submission_hypo.to_csv("submission_submission_hp_v4.csv", index=False)

!kaggle competitions submit -c bike-sharing-demand -f submission_submission_hp_v4.csv -m "new hyperparameters V4  "
!kaggle competitions submissions -c bike-sharing-demand

Successfully submitted to Bike Sharing Demand

c:\users\hp\anaconda3\envs\aws  deep learning\lib\site-packages\cryptography\hazmat\backends\openssl\x509.py:17: CryptographyDeprecationWarning: This version of cryptography contains a temporary pyOpenSSL fallback path. Upgrade pyOpenSSL now.
  utils.DeprecatedIn35,

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  4%|4         | 8.00k/194k [00:00<00:03, 53.7kB/s]
 49%|####9     | 96.0k/194k [00:00<00:00, 450kB/s] 
100%|##########| 194k/194k [00:00<00:00, 664kB/s] 
100%|##########| 194k/194k [00:02<00:00, 85.4kB/s]

fileName                                 date                 description                                   status    publicScore  privateScore  
---------------------------------------  -------------------  --------------------------------------------  --------  -----------  ------------  
submission_submission_hp_v4.csv          2021-12-11 15:59:00  new hyperparameters V4                        complete  0.44957      0.44957       
submission_time_search_5min.csv          2021-12-11 10:12:57  5 min train submit                            complete  0.44793      0.44793       
submission_submission_hp_v4.csv          2021-12-11 09:09:50  new hyperparameters V4                        complete  0.48757      0.48757       
submission_submission_hp_v4.csv          2021-12-11 08:52:06  new hyperparameters V4                        complete  0.48080      0.48080       
submission.csv                           2021-12-11 08:34:12  first raw submission                          complete  1.40041      1.40041       
submission_submission_hp_v4.csv          2021-12-10 18:11:50  new hyperparameters V4                        complete  0.45257      0.45257       
submission_submission_hp_v4.csv          2021-12-10 16:42:13  new hyperparameters V4                        complete  0.46035      0.46035       
submission_submission_RMSLE.csv          2021-12-10 14:44:00  Custome Eval Metric                           complete  0.45892      0.45892       
submission_submission_hypov2.csv         2021-12-10 14:23:31  new features with hyperparameters V2          complete  0.45490      0.45490       
submission_submission_hypov2.csv         2021-12-10 13:57:06  new features with hyperparameters V2          complete  0.57647      0.57647       
submission_submission_hypo.csv           2021-12-10 10:07:22  new features with hyperparameters             complete  0.45563      0.45563       
submission_time_search_5min.csv          2021-12-10 09:23:07  5 min train submit                            complete  0.44797      0.44797       
submission_import_features.csv           2021-12-08 13:55:39  Feature Importance                            complete  0.45194      0.45194       
submission_import_features.csv           2021-12-08 13:53:11  Feature Importance                            complete  0.45414      0.45414       
submission_import_features.csv           2021-12-08 13:51:11  Feature Importance                            complete  0.45749      0.45749       
submission_import_features.csv           2021-12-08 13:48:53  Feature Importance                            complete  0.45210      0.45210       
submission_import_features.csv           2021-12-08 13:44:55  Feature Importance                            complete  0.48275      0.48275       
submission_import_features.csv           2021-12-08 13:42:28  Feature Importance                            complete  0.45753      0.45753       
submission_import_features.csv           2021-12-08 13:39:01  Feature Importance                            complete  0.45939      0.45939       
submission_new_featuresV3.csv            2021-12-08 12:44:41  new features_AdvancedV3                       complete  0.45274      0.45274       
submission_new_features_Advanced_V1.csv  2021-12-08 12:20:43  new features Advanced V1                      complete  0.45810      0.45810       
submission_new_features_Advanced_V1.csv  2021-12-08 12:20:12  new features                                  complete  0.45810      0.45810       
submission_new_features_log.csv          2021-12-08 12:07:08  NF - Model 2  (Basic  Log Transform )         complete  0.50789      0.50789       
submission_new_features_log.csv          2021-12-08 12:06:06  NF - Model 2  (Basic  Log Transform )         complete  0.50674      0.50674       
submission_new_features_log.csv          2021-12-08 11:34:53  NF - Model 2  (Basic  Log Transform )         complete  0.50674      0.50674       
submission_new_features.csv              2021-12-08 11:24:55  NF - Model 1  (Basic  Time Catogricla data )  complete  0.50674      0.50674       
submission.csv                           2021-12-08 11:13:27  first raw submission                          complete  1.38952      1.38952       
submission_new_hpo.csv                   2021-12-08 11:06:33  new features with hyperparameters Tune        complete  0.48748      0.48748       
submission_new_hpo.csv                   2021-12-08 09:14:10  new features with hyperparameters Tune        complete  0.51858      0.51858       
submission_new_hpo_ensV4.csv             2021-12-07 15:02:09  new features with hyperparameters Vקרדןםמ 4   complete  0.48961      0.48961       
submission_new_hpo.csv                   2021-12-07 14:29:38  new features with hyperparameters Tune        complete  0.48338      0.48338       
submission_new_featuresV3.csv            2021-12-07 14:00:06  new featuresV3                                complete  0.45074      0.45074       
submission_new_featuresV2.csv            2021-12-07 13:39:45  new features                                  complete  0.50306      0.50306       
submission_new_features.csv              2021-12-07 13:09:48  new features                                  complete  0.51097      0.51097       
submission.csv                           2021-12-07 13:04:23  first raw submission                          complete  1.39309      1.39309       
submission_new_hpo.csv                   2021-12-07 11:12:13  new features with hyperparameters Tune        complete  0.45509      0.45509       
submission_new_featuresV3.csv            2021-12-07 10:53:43  new featuresV3                                complete  0.45057      0.45057       
submission_new_featuresV2.csv            2021-12-07 10:43:05  new features                                  complete  0.47595      0.47595       
submission_new_featuresV2.csv            2021-12-07 10:42:32  new features                                  error     None         None          
submission_new_features.csv              2021-12-07 10:32:26  new features                                  complete  0.50670      0.50670       
submission.csv                           2021-12-07 10:24:27  first raw submission                          complete  1.38989      1.38989       
submission_new_hpo.csv                   2021-12-07 09:08:34  new features with hyperparameters             complete  0.46172      0.46172       
submission_new_hpo.csv                   2021-12-07 08:59:07  new features with hyperparameters             complete  0.45760      0.45760       
submission_new_features.csv              2021-12-06 20:57:01  new features                                  complete  0.44755      0.44755       
submission_new_features.csv              2021-12-06 20:50:18  new features                                  complete  0.44577      0.44577       
submission_new_features.csv              2021-12-06 20:45:43  new features                                  complete  0.44592      0.44592       
submission_RMSLE.csv                     2021-12-06 17:57:57  Custome Eval Metric                           complete  0.46093      0.46093       
submission_RMSLE.csv                     2021-12-06 17:46:31  Custome Eval Metric                           complete  0.46093      0.46093       
submission_RMSLE.csv                     2021-12-06 17:25:04  Custome Eval Metric                           complete  0.46093      0.46093       
submission_RMSLE.csv                     2021-12-06 17:13:44  Custome Eval Metric                           complete  0.46093      0.46093

c:\users\hp\anaconda3\envs\aws  deep learning\lib\site-packages\cryptography\hazmat\backends\openssl\x509.py:17: CryptographyDeprecationWarning: This version of cryptography contains a temporary pyOpenSSL fallback path. Upgrade pyOpenSSL now.
  utils.DeprecatedIn35,

Step 7: Write a Report¶

Refer to the markdown file for the full report¶

Creating plots and table for report¶

# Taking the top model score from each training run and creating a line plot to show improvement
# You can create these in the notebook and save them to PNG or use some other tool (e.g. google sheets, excel)
fig = pd.DataFrame(
    {
        "model": ["initial", "add_features", "hpo"],
        "score": [118.456660, 47.885773, 47.877308]
    }
).plot(x="model", y="score", figsize=(8, 6)).get_figure()
fig.savefig('model_train_score.png')

# Take the 3 kaggle scores and creating a line plot to show improvement
fig = pd.DataFrame(
    {
        "test_eval": ["initial", "add_features", "hpo"],
        "score": [1.3, 0.451, 0.44793]
    }
).plot(x="test_eval", y="score", figsize=(8, 6)).get_figure()
fig.savefig('model_test_score.png')

Hyperparameter table¶

# The 3 hyperparameters we tuned with the kaggle score as the result
pd.DataFrame({
    "model": ["initial", "add_features", "hpo"],
    "hpo1": [?, ?, ?],
    "hpo2": [?, ?, ?],
    "hpo3": [?, ?, ?],
    "score": [?, ?, ?]
})

	season	holiday	workingday	weather	temp	atemp	humidity	windspeed	casual	registered	count
count	10886.000000	10886.000000	10886.000000	10886.000000	10886.00000	10886.000000	10886.000000	10886.000000	10886.000000	10886.000000	10886.000000
mean	2.506614	0.028569	0.680875	1.418427	20.23086	23.655084	61.886460	12.799395	36.021955	155.552177	191.574132
std	1.116174	0.166599	0.466159	0.633839	7.79159	8.474601	19.245033	8.164537	49.960477	151.039033	181.144454
min	1.000000	0.000000	0.000000	1.000000	0.82000	0.760000	0.000000	0.000000	0.000000	0.000000	1.000000
25%	2.000000	0.000000	0.000000	1.000000	13.94000	16.665000	47.000000	7.001500	4.000000	36.000000	42.000000
50%	3.000000	0.000000	1.000000	1.000000	20.50000	24.240000	62.000000	12.998000	17.000000	118.000000	145.000000
75%	4.000000	0.000000	1.000000	2.000000	26.24000	31.060000	77.000000	16.997900	49.000000	222.000000	284.000000
max	4.000000	1.000000	1.000000	4.000000	41.00000	45.455000	100.000000	56.996900	367.000000	886.000000	977.000000

	datetime	season	workingday	weather	temp	atemp	humidity	windspeed
0	2011-01-20 00:00:00	1	1	1	10.66	11.365	56	26.0027
1	2011-01-20 01:00:00	1	1	1	10.66	13.635	56	0.0000
2	2011-01-20 02:00:00	1	1	1	10.66	13.635	56	0.0000
3	2011-01-20 03:00:00	1	1	1	10.66	12.880	56	11.0014
4	2011-01-20 04:00:00	1	1	1	10.66	12.880	56	11.0014

	datetime	count
0	2011-01-20 00:00:00	0
1	2011-01-20 01:00:00	0
2	2011-01-20 02:00:00	0
3	2011-01-20 03:00:00	0
4	2011-01-20 04:00:00	0

	model	score_test	score_val	pred_time_test	pred_time_val	fit_time	pred_time_test_marginal	pred_time_val_marginal	fit_time_marginal	stack_level	can_infer	fit_order
0	RandomForestMSE_BAG_L1	-43.634011	-118.456660	0.632069	0.350819	2.670399	0.632069	0.350819	2.670399	1	True	5
1	WeightedEnsemble_L2	-43.634011	-118.456660	0.640065	0.351823	2.990219	0.007997	0.001004	0.319819	2	True	6
2	LightGBM_BAG_L2	-64.027416	-117.571913	18.166591	2.978304	39.001745	0.319816	0.056966	3.523970	2	True	8
3	WeightedEnsemble_L3	-64.217731	-117.101612	19.020701	3.112224	44.428629	0.007769	0.000000	0.212890	3	True	10
4	LightGBMXT_BAG_L2	-65.338738	-117.536940	18.693117	3.055258	40.691769	0.846342	0.133920	5.213994	2	True	7
5	RandomForestMSE_BAG_L2	-65.391408	-120.872583	18.446479	3.348073	41.980420	0.599704	0.426736	6.502644	2	True	9
6	KNeighborsDist_BAG_L1	-68.114477	-169.552096	0.131080	0.112583	0.044974	0.131080	0.112583	0.044974	1	True	2
7	LightGBM_BAG_L1	-102.898965	-132.344251	1.797965	0.213881	7.078919	1.797965	0.213881	7.078919	1	True	4
8	LightGBMXT_BAG_L1	-116.342281	-134.100042	15.162717	2.131772	25.636512	15.162717	2.131772	25.636512	1	True	3
9	KNeighborsUnif_BAG_L1	-130.801229	-160.413078	0.122945	0.112283	0.046972	0.122945	0.112283	0.046972	1	True	1

	importance	stddev	p_value	n	p99_high	p99_low
datetime	114.578957	2.056792	0.000054	3	126.364603	102.793312
humidity	85.476958	4.246491	0.000411	3	109.809820	61.144096
atemp	80.665268	3.179611	0.000259	3	98.884790	62.445746
temp	47.347653	1.602999	0.000191	3	56.533016	38.162289
windspeed	30.944962	4.054998	0.002838	3	54.180549	7.709375
workingday	15.309436	2.559048	0.004593	3	29.973063	0.645810
weather	10.638480	1.189232	0.002070	3	17.452911	3.824049
holiday	0.743894	0.076601	0.001758	3	1.182825	0.304963
season	0.543100	1.316102	0.274467	3	8.084510	-6.998310

	datetime	season	weather	temp	atemp	humidity	casual	registered	count
0	2011-01-01 00:00:00	1	1	9.84	14.395	81	3	13	16
1	2011-01-01 01:00:00	1	1	9.02	13.635	80	8	32	40
2	2011-01-01 02:00:00	1	1	9.02	13.635	80	5	27	32
3	2011-01-01 03:00:00	1	1	9.84	14.395	75	3	10	13
4	2011-01-01 04:00:00	1	1	9.84	14.395	75	0	1	1

	model	score_test	score_val	pred_time_test	pred_time_val	fit_time	pred_time_test_marginal	pred_time_val_marginal	fit_time_marginal	stack_level	can_infer	fit_order
0	LightGBM_BAG_L2	-13.931066	-34.063639	14.252502	2.407869	36.289093	0.602653	0.148915	4.341497	2	True	8
1	WeightedEnsemble_L3	-13.955036	-33.816883	16.325964	3.124473	51.073545	0.008994	0.000999	0.223871	3	True	10
2	ExtraTreesMSE_BAG_L1	-13.963567	-37.907656	0.606763	0.410764	1.959871	0.606763	0.410764	1.959871	1	True	5
3	RandomForestMSE_BAG_L1	-14.159395	-38.415614	0.677047	0.383782	4.031678	0.677047	0.383782	4.031678	1	True	4
4	WeightedEnsemble_L2	-14.780175	-33.598879	13.402286	2.032838	32.200451	0.008996	0.000000	0.318815	2	True	6
5	LightGBMXT_BAG_L2	-14.891751	-35.081237	14.948757	2.485825	38.539795	1.298908	0.226870	6.592200	2	True	7
6	RandomForestMSE_BAG_L2	-15.514304	-34.479085	14.415409	2.747689	39.915977	0.765560	0.488734	7.968381	2	True	9
7	LightGBMXT_BAG_L1	-16.404830	-34.479591	12.109479	1.238292	25.890086	12.109479	1.238292	25.890086	1	True	3
8	KNeighborsDist_BAG_L1	-107.626155	-179.133407	0.130545	0.112663	0.032980	0.130545	0.112663	0.032980	1	True	2
9	KNeighborsUnif_BAG_L1	-136.323106	-164.889972	0.126014	0.113454	0.032980	0.126014	0.113454	0.032980	1	True	1

	importance	stddev	p_value	n	p99_high	p99_low
hour	197.170591	3.534212	0.000054	3	217.422016	176.919166
workingday	67.957786	2.856578	0.000294	3	84.326298	51.589275
date	34.485488	2.689452	0.001011	3	49.896344	19.074632
datetime	33.603976	2.187511	0.000705	3	46.138653	21.069298
weekday	22.583808	0.668894	0.000146	3	26.416645	18.750971
humidity	22.122182	0.923877	0.000290	3	27.416100	16.828264
temp	17.940420	0.912210	0.000430	3	23.167483	12.713358
atemp	16.174898	1.225393	0.000954	3	23.196534	9.153262
weather	15.634848	1.741819	0.002056	3	25.615663	5.654032
month	10.988939	0.464586	0.000298	3	13.651070	8.326809
day	10.826542	0.547731	0.000426	3	13.965101	7.687983
season	2.900745	0.341267	0.002291	3	4.856245	0.945246
windspeed	2.874331	0.242800	0.001185	3	4.265599	1.483063
holiday	1.269797	0.357387	0.012702	3	3.317662	-0.778068

	model	score_test	score_val	pred_time_test	pred_time_val	fit_time	pred_time_test_marginal	pred_time_val_marginal	fit_time_marginal	stack_level	can_infer	fit_order
0	WeightedEnsemble_L3	-13.886557	-33.782797	25.962895	4.207922	54.143911	0.007994	0.001000	0.234878	3	True	10
1	LightGBM_BAG_L2	-14.055400	-33.982358	23.336702	3.356391	38.533814	0.552681	0.146912	4.965160	2	True	8
2	ExtraTreesMSE_BAG_L1	-14.068774	-38.193369	0.622889	0.375784	1.992852	0.622889	0.375784	1.992852	1	True	5
3	RandomForestMSE_BAG_L1	-14.150597	-38.397120	0.547840	0.378769	4.485416	0.547840	0.378769	4.485416	1	True	4
4	LightGBMXT_BAG_L2	-14.526568	-34.948988	24.865172	3.519319	41.320181	2.081151	0.309839	7.751527	2	True	7
5	WeightedEnsemble_L2	-14.772862	-33.615160	22.535855	2.983355	33.852499	0.007997	0.001000	0.349798	2	True	6
6	RandomForestMSE_BAG_L2	-15.663307	-34.615328	23.321068	3.750170	41.192347	0.537048	0.540691	7.623692	2	True	9
7	LightGBMXT_BAG_L1	-16.337520	-34.477709	21.357129	2.227803	27.024433	21.357129	2.227803	27.024433	1	True	3
8	KNeighborsDist_BAG_L1	-107.886008	-179.788349	0.131027	0.113635	0.032972	0.131027	0.113635	0.032972	1	True	2
9	KNeighborsUnif_BAG_L1	-137.078940	-165.401563	0.125135	0.113488	0.032981	0.125135	0.113488	0.032981	1	True	1

	model	score_test	score_val	pred_time_test	pred_time_val	fit_time	pred_time_test_marginal	pred_time_val_marginal	fit_time_marginal	stack_level	can_infer	fit_order
0	KNeighborsDist_BAG_L1	-0.283889	-72.135046	0.127982	0.112256	0.061965	0.127982	0.112256	0.061965	1	True	2
1	LightGBM_BAG_L1	-8.646669	-38.245361	10.174142	1.136365	31.409857	10.174142	1.136365	31.409857	1	True	3
2	WeightedEnsemble_L2	-8.646669	-38.245361	10.182119	1.136365	31.659730	0.007977	0.000000	0.249873	2	True	4
3	WeightedEnsemble_L3	-11.524802	-37.668018	15.240383	2.450974	58.355911	0.007996	0.000999	0.223883	3	True	8
4	LightGBM_BAG_L2	-11.645646	-38.912343	10.909840	1.497464	36.941634	0.482703	0.136920	5.409848	2	True	6
5	LightGBMXT_BAG_L2	-12.640081	-38.202572	14.047070	1.816336	43.267025	3.619934	0.455793	11.735239	2	True	5
6	RandomForestMSE_BAG_L2	-13.324610	-39.092819	11.129750	1.857261	40.986941	0.702614	0.496718	9.455155	2	True	7
7	KNeighborsUnif_BAG_L1	-58.788918	-72.685921	0.125013	0.111923	0.059965	0.125013	0.111923	0.059965	1	True	1

	importance	stddev	p_value	n	p99_high	p99_low
shift_7	128.675816	3.843351	0.000149	3	150.698641	106.652991
shift_1	80.366382	3.466760	0.000310	3	100.231302	60.501462
hour	36.030048	1.055016	0.000143	3	42.075406	29.984691
weather	23.518991	1.156033	0.000402	3	30.143185	16.894796
workingday	21.823440	3.372230	0.003933	3	41.146692	2.500187
humidity	10.719177	0.788247	0.000899	3	15.235921	6.202433
weekday	8.899389	0.645688	0.000875	3	12.599252	5.199525
shift_2	7.893582	1.298439	0.004450	3	15.333783	0.453382
shift_30	6.884801	2.185347	0.015991	3	19.407082	-5.637480
holiday	6.084657	3.805140	0.054692	3	27.888529	-15.719214
atemp	3.967531	0.862529	0.007696	3	8.909920	-0.974859
temp	3.872622	0.945149	0.009641	3	9.288429	-1.543186
datetime	3.251437	0.468886	0.003430	3	5.938207	0.564666
day	3.176005	1.014712	0.016191	3	8.990417	-2.638407
season	2.909107	0.496893	0.004793	3	5.756359	0.061854
date	2.808964	0.576545	0.006877	3	6.112631	-0.494703
month	2.265374	0.185249	0.001111	3	3.326871	1.203877
windspeed	2.102785	0.443802	0.007263	3	4.645819	-0.440249

	model	score_test	score_val	pred_time_test	pred_time_val	fit_time	pred_time_test_marginal	pred_time_val_marginal	fit_time_marginal	stack_level	can_infer	fit_order
0	KNeighborsDist_BAG_L1	-0.000000	-47.578748	4.324185	5.020104	0.026984	4.324185	5.020104	0.026984	1	True	2
1	RandomForestMSE_BAG_L1	-14.953333	-40.409168	0.526908	0.534705	12.890577	0.526908	0.534705	12.890577	1	True	5
2	LightGBMXT_BAG_L2	-19.481544	-33.398709	15.264128	11.105198	45.245967	0.790564	0.175928	4.644301	2	True	7
3	WeightedEnsemble_L3	-19.481544	-33.398709	15.265128	11.106197	45.247964	0.000999	0.000999	0.001998	3	True	8
4	LightGBMXT_BAG_L1	-19.641388	-31.714531	3.835796	0.477777	18.554368	3.835796	0.477777	18.554368	1	True	3
5	WeightedEnsemble_L2	-19.711362	-31.636736	9.381241	5.734774	27.997924	0.003979	0.001013	0.308821	2	True	6
6	LightGBM_BAG_L1	-23.690134	-34.422494	1.217281	0.235880	9.107751	1.217281	0.235880	9.107751	1	True	4
7	KNeighborsUnif_BAG_L1	-38.689969	-48.240770	4.569395	4.660803	0.021986	4.569395	4.660803	0.021986	1	True	1

	model	score_test	score_val	pred_time_test	pred_time_val	fit_time	pred_time_test_marginal	pred_time_val_marginal	fit_time_marginal	stack_level	can_infer	fit_order
0	KNeighborsDist_BAG_L1	-3.270174e-07	-47.885773	4.354761	4.518684	0.019987	4.354761	4.518684	0.019987	1	True	2
1	RandomForestMSE_BAG_L1	-1.482808e+01	-40.070932	0.509412	0.431772	11.071623	0.509412	0.431772	11.071623	1	True	5
2	LightGBMXT_BAG_L2	-1.684611e+01	-32.632991	16.961518	10.542506	45.697771	1.560102	0.244818	6.478295	2	True	7
3	WeightedEnsemble_L3	-1.684611e+01	-32.632991	16.968516	10.543504	45.699769	0.006997	0.000999	0.001998	3	True	8
4	WeightedEnsemble_L2	-1.896510e+01	-31.560907	10.585708	5.297238	28.435670	0.011993	0.001000	0.306823	2	True	6
5	LightGBMXT_BAG_L1	-1.925119e+01	-31.657134	4.356027	0.510692	17.362014	4.356027	0.510692	17.362014	1	True	3
6	LightGBM_BAG_L1	-2.062035e+01	-33.903901	1.862927	0.266862	10.746845	1.862927	0.266862	10.746845	1	True	4
7	KNeighborsUnif_BAG_L1	-3.891666e+01	-48.505319	4.318290	4.569677	0.019007	4.318290	4.569677	0.019007	1	True	1

	model	score_test	score_val	pred_time_test	pred_time_val	fit_time	pred_time_test_marginal	pred_time_val_marginal	fit_time_marginal	stack_level	can_infer	fit_order
0	KNeighborsDist_BAG_L1	-2.068240e-07	-47.877308	4.335899	4.760259	0.025984	4.335899	4.760259	0.025984	1	True	2
1	ExtraTreesMSE_BAG_L1	-1.424137e+01	-38.633465	0.641069	0.429775	3.177173	0.641069	0.429775	3.177173	1	True	7
2	RandomForestMSE_BAG_L1	-1.476350e+01	-39.950767	0.649731	0.397770	11.095634	0.649731	0.397770	11.095634	1	True	5
3	LightGBMXT_BAG_L2	-1.598340e+01	-32.417824	15.564713	10.937941	158.161819	1.248281	0.223871	9.813329	2	True	9
4	WeightedEnsemble_L3	-1.681246e+01	-31.703901	16.649247	11.558583	169.613444	0.007910	0.000999	0.294821	3	True	14
5	RandomForestMSE_BAG_L2	-1.701187e+01	-32.695044	14.977512	11.295735	164.052448	0.661080	0.581664	15.703958	2	True	11
6	ExtraTreesMSE_BAG_L2	-1.756622e+01	-31.979331	14.964303	11.205789	152.325426	0.647871	0.491719	3.976936	2	True	13
7	LightGBM_BAG_L1	-1.760263e+01	-33.671413	1.693025	0.235863	13.246117	1.693025	0.235863	13.246117	1	True	4
8	CatBoost_BAG_L2	-1.777008e+01	-32.895921	14.490366	10.806980	186.469009	0.173934	0.092910	38.120519	2	True	12
9	LightGBM_BAG_L2	-1.800741e+01	-32.158074	14.745185	10.841994	155.528358	0.428753	0.127924	7.179868	2	True	10
10	WeightedEnsemble_L2	-1.901976e+01	-31.591619	8.802482	5.479951	134.451462	0.007815	0.000999	0.393772	2	True	8
11	LightGBMXT_BAG_L1	-1.939929e+01	-31.748761	2.495561	0.387892	17.197382	2.495561	0.387892	17.197382	1	True	3
12	CatBoost_BAG_L1	-3.100732e+01	-36.213989	0.270181	0.094939	103.588206	0.270181	0.094939	103.588206	1	True	6
13	KNeighborsUnif_BAG_L1	-3.893895e+01	-48.481624	4.230965	4.407574	0.017992	4.230965	4.407574	0.017992	1	True	1

	model	score_test	score_val	pred_time_test	pred_time_val	fit_time	pred_time_test_marginal	pred_time_val_marginal	fit_time_marginal	stack_level	can_infer	fit_order
0	KNeighborsDist_BAG_L1\T0	-2.068240e-07	-47.877308	4.871578	10.032989	0.018990	4.871578	10.032989	0.018990	1	True	2
1	LightGBMLarge_BAG_L1	-8.014737e+00	-35.225451	4.323074	0.792816	134.373873	4.323074	0.792816	134.373873	1	True	6
2	ExtraTreesMSE_BAG_L1\T0	-1.424137e+01	-38.633465	1.109482	0.995581	5.977026	1.109482	0.995581	5.977026	1	True	4
3	LightGBMLarge_BAG_L2	-1.433855e+01	-33.019492	20.289999	23.174163	431.150962	1.347229	0.197875	28.024499	2	True	11
4	RandomForestMSE_BAG_L1\T0	-1.476350e+01	-39.950767	1.041012	0.922008	19.216448	1.041012	0.922008	19.216448	1	True	3
5	WeightedEnsemble_L3	-1.537694e+01	-31.586497	25.311931	25.573819	602.117391	0.010432	0.000000	0.325811	3	True	12
6	ExtraTreesMSE_BAG_L2\T0	-1.578517e+01	-32.073201	19.988609	23.927739	408.816188	1.045839	0.951452	5.689725	2	True	9
7	RandomForestMSE_BAG_L2\T0	-1.579074e+01	-32.583210	19.961134	23.924740	420.671361	1.018364	0.948453	17.544898	2	True	8
8	NeuralNetFastAI_BAG_L2\T0	-1.658940e+01	-32.452041	21.890068	23.476038	550.532458	2.947298	0.499751	147.405995	2	True	10
9	WeightedEnsemble_L2	-1.779631e+01	-31.629045	11.696228	11.617661	378.296793	0.011012	0.000980	0.388791	2	True	7
10	NeuralNetFastAI_BAG_L1\T0	-2.423508e+01	-32.854140	2.490565	0.790876	243.515140	2.490565	0.790876	243.515140	1	True	5
11	KNeighborsUnif_BAG_L1\T0	-3.893895e+01	-48.481624	5.107059	9.442017	0.024986	5.107059	9.442017	0.024986	1	True	1

	datetime	season	weather	temp	atemp	humidity	count	...	shift_4h	shift_5h	rollin_7d_mean	rollin_7d_std	rollin_30d_mean	rollin_30d_std	rollin_12h_mean	rollind_12h_std	rollin_24h_mean	rollind_24h_std
0	2011-01-01 00:00:00	1	1	9.84	14.395	81	16.0	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	2011-01-01 01:00:00	1	1	9.02	13.635	80	40.0	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	2011-01-01 02:00:00	1	1	9.02	13.635	80	32.0	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	2011-01-01 03:00:00	1	1	9.84	14.395	75	13.0	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	2011-01-01 04:00:00	1	1	9.84	14.395	75	1.0	...	16.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

	model	score_test	score_val	pred_time_test	pred_time_val	fit_time	pred_time_test_marginal	pred_time_val_marginal	fit_time_marginal	stack_level	can_infer	fit_order
0	LightGBM_BAG_L1\T4	-17.815342	-34.169053	1.708016	0.275843	7.039939	1.708016	0.275843	7.039939	1	True	5
1	LightGBM_BAG_L1\T1	-17.977535	-34.134480	1.160332	0.247857	5.996946	1.160332	0.247857	5.996946	1	True	2
2	LightGBM_BAG_L2\T31	-18.435657	-34.080935	102.471532	15.025370	182.271012	0.799539	0.199886	11.675263	2	True	51
3	LightGBM_BAG_L2\T7	-18.547666	-34.119909	101.918851	14.898440	173.909827	0.246858	0.072956	3.314078	2	True	27
4	LightGBM_BAG_L2\T4	-18.647891	-34.129373	101.999804	14.906437	174.027758	0.327811	0.080954	3.432009	2	True	24
...	...	...	...	...	...	...	...	...	...	...	...	...
246	LightGBM_BAG_L4\T24	-19.495032	-35.557360	151.717381	27.881863	769.063504	0.287834	0.093946	4.956145	4	True	177
247	LightGBM_BAG_L4\T51	-19.502258	-35.553092	151.716381	27.881861	769.354323	0.286835	0.093944	5.246965	4	True	204
248	LightGBM_BAG_L4\T34	-19.503884	-35.686382	151.769351	27.883861	769.362318	0.339805	0.095944	5.254960	4	True	187
249	LightGBM_BAG_L1\T5	-19.701515	-33.618542	7.228837	1.044400	9.986228	7.228837	1.044400	9.986228	1	True	6
250	LightGBM_BAG_L1\T12	-19.715778	-33.701111	3.340077	0.566677	8.713440	3.340077	0.566677	8.713440	1	True	13